Saffron Authentication Research Articles

Quality and authenticity of saffron and sensory aspects

Quality and authenticity of saffron and sensory aspects

Non-targeted volatilomics for the authentication of saffron by gas chromatography-ion mobility spectrometry and multivariate curve resolution

Non-targeted volatilomics for the authentication of saffron by gas chromatography-ion mobility spectrometry and multivariate curve resolution

Time-of-Flight Secondary Ion Mass Spectrometry Coupled with Unsupervised Methods for Advanced Saffron Authenticity Screening.

Saffron, renowned for its aroma and flavor, is susceptible to adulteration due to its high value and demand. Current detection methods, including ISO standards, often fail to identify specific adulterants such as safflower or turmeric up to 20% (w/w). Therefore, the quest continues for robust screening methods using advanced techniques to tackle this persistent challenge of safeguarding saffron quality and authenticity. Advanced techniques such as time-of-flight secondary ion mass spectrometry (TOF-SIMS), with its molecular specificity and high sensitivity, offer promising solutions. Samples of pure saffron and saffron adulterated with safflower and turmeric at three inclusion levels (5%, 10%, and 20%) were analyzed without prior treatment. Spectral analysis revealed distinct signatures for pure saffron, safflower, and turmeric. Through principal component analysis (PCA), TOF-SIMS effectively discriminated between pure saffron and saffron adulterated with turmeric and safflower at different inclusion levels. The variation between the groups is attributed to the characteristic peaks of safflower and the amino group peaks and mineral peaks of saffron. Additionally, a study was conducted to demonstrate that semi-quantification of the level of safflower inclusion can be achieved from the normalized values of its characteristic peaks in the saffron matrix.

Geographical authentication of saffron by chemometrics applied to the ion mobility spectrometry data

There is a lack of a reliable tool for quickly determining the geographical origins of saffron (SFR). Ion mobility spectrometry (IMS) has emerged as a promising method for rapid authentication. In this study, 232 Iranian SFR samples harvested in five distinct areas (Khorasan, Azerbaijan, Golestan, Fars, and Isfahan) were analyzed by IMS coupled with chemometric methods. The principal component analysis (PCA) was applied for analyzing the collected IMS data, utilizing three principle components (PCs) that accounted for 81 % of the explained variance. Moreover, the partial least squares-discriminant analysis (PLS-DA) demonstrated the average sensitivity and specificity rates, of 72.3 % to 92.5 % for the exernal test set and 75.5 % to 94.3 % for training set. The accuracy values were ≥ 85.0 % for the prediction set for all classes of samples. The results of this study revealed a successful application of IMS and chemometric methods for rapid geographical authentication of saffron samples in Iran.

An optical-nose device based on fluorescent nanomaterials sensor array for authentication of saffron

This paper presents the development of an optical-nose (o-nose) fluorescence sensor array for the authentication of saffron. The sensor array, which consists of fluorescence sensors made from chemically modified metallic nanoclusters and carbon dots of various functional groups, sniffs the odor emitted from saffron and other possible adulterant herbs and accordingly produces differential fluorescence patterns for different herbs. According to the difference in the chemical composition of the herbs’ aroma, the device was able to differentiate saffron from other herbs and pure saffron from its mixtures with other herbs. The device produces a multi-dimensional response vector and hence it needs to be processed by multivariate data analysis methods. Classification modeling by linear discriminant analysis, principal component-discriminant analysis, and partial least squares discriminant analysis were successfully utilized to model the sensor data and accurate discriminate models were produced. Additionally, the method was able to differentiate between four different organs of saffron which differ in quality. The results of this study demonstrate the potential of o-nose fluorescence sensor arrays for the rapid and reliable authentication of saffron.

Detection of saffron adulteration by other plant species using SPME-GC-HRMS

Saffron, a spice obtained by drying the scars of saffron flowers (Crocus sativus L.), is by far the most valuable spice in the world, and, not surprisingly, it often becomes an object of adulteration. In this study, non-target screening of volatile compounds in 38 authentic saffron samples and 25 samples of plant materials, potential saffron adulterants (safflower, calendula, capsicum and turmeric) was performed using headspace solid-phase microextraction for sample extraction followed by gas chromatography coupled to high-resolution mass spectrometry (HS-SPME-GC-HRMS) for non-target volatiles screening. The chemometric analysis of generated data by principal component analysis (PCA) and partial least squares discriminative analysis (PLS-DA) showed good separation of authentic saffron from potential plant adulterants. Significant compounds (‘markers’) for each group of these plants, as well as for saffron were tentatively identified. The target screening of selected ‘markers’ in model admixtures enabled simple and reliable detection levels as low as 2% w/w of safflower, calendula, capsicum or turmeric present in saffron.

Detection of saffron adulteration with Crocus sativus style using NIR-hyperspectral imaging and chemometrics

Saffron is a valuable spice that is often adulterated. This study proposes using near-infrared hyperspectral imaging (NIR-HSI) and chemometrics as a fast and cost-effective method for detecting and quantifying adulteration in saffron stigmas. Adulterated saffron samples were prepared by adding Crocus sativus style to pure saffron stigmas in varying concentrations (20–90%). The spectral data were pre-treated using standard normal variate (SNV), and multiplicative scatter correction (MSC), while variable reduction was performed by Principal Component Analysis (PCA) and Partial Least Squares (PLS). Classification was done using Linear Discriminant Analysis (LDA), PLS-DA, Support Vector Machine (SVM), and Multi-layer Perceptron (MLP) models, while quantification was achieved by PLS, PCA, SVM, and MLP-based regression models. The HSI technique achieved correct classification rates of 95.6%–100% in discriminating authentic saffron from plant adulterants and adulterated saffron across all the models. Regression models to quantify the percentage style adulteration in saffron demonstrated excellent prediction abilities with almost all models achieving RPD (Residual Predictive Deviation) values of 3.0–5.4. The MLP model (1 hidden layer with 3 neurons) built from SNV pre-processed and PLS reduced data (15 LVs), showed exceptional predictive capabilities, with an R2p of 0.97, a Root Mean Squared Error of Prediction (RMSEP) of 4.3%, and an RPD of 5.4. The results demonstrate the potential of NIR-HSI and chemometrics for rapid and nondestructive detection and quantification of style in saffron stigmas.

Digging for gold: evaluating the authenticity of saffron (Crocus sativus L.) via deep learning optimization

IntroductionSaffron is one of the most coveted and one of the most tainted products in the global food market. A major challenge for the saffron industry is the difficulty to distinguish between adulterated and authentic dried saffron along the supply chain. Current approaches to analyzing the intrinsic chemical compounds (crocin, picrocrocin, and safranal) are complex, costly, and time-consuming. Computer vision improvements enabled by deep learning have emerged as a potential alternative that can serve as a practical tool to distinguish the pureness of saffron.MethodsIn this study, a deep learning approach for classifying the authenticity of saffron is proposed. The focus was on detecting major distinctions that help sort out fake samples from real ones using a manually collected dataset that contains an image of the two classes (saffron and non-saffron). A deep convolutional neural model MobileNetV2 and Adaptive Momentum Estimation (Adam) optimizer were trained for this purpose.ResultsThe observed metrics of the deep learning model were: 99% accuracy, 99% recall, 97% precision, and 98% F-score, which demonstrated a very high efficiency.DiscussionA discussion is provided regarding key factors identified for obtaining positive results. This novel approach is an efficient alternative to distinguish authentic from adulterated saffron products, which may be of benefit to the saffron industry from producers to consumers and could serve to develop models for other spices.

Rapid authentication of intact saffron stigma thorough the package using Vis-SWNIR hyperspectral imaging coupled with chemometrics

This study presents a rapid method for detecting and authenticating intact saffron stigma through its packaging using visible-short wavelength near-infrared (Vis-SWNIR) hyperspectral imaging (HSI) in conjunction with chemometrics. A dataset containing 38 authentic saffron samples was utilized, and HSI images were captured within the spectral range of 400–950 nm. Multivariate curve resolution-alternating least squares (MCR-ALS) was used to obtain pure spatial and spectral profiles of the components. The resolved spatial profiles were then subjected to principal component analysis (PCA) to identify potential patterns in the authentic samples. Binary mixtures of pooled saffron samples and adulterants, including safflower, calendula, and saffron style, were prepared at varying levels. Data-driven soft independent modeling of class analogy (DD-SIMCA) was employed to establish a boundary between authentic and adulterated samples, resulting in robust classification performance with a sensitivity of 95 % and specificity of 100 %. Partial least squares-discriminant analysis (PLS-DA) was used for binary and multi-class classification of the MCR-ALS resolved spatial profiles. The performance of PLS-DA was evaluated using sensitivity, specificity, and accuracy measures, which exceeded 83.4 % for the calibration, cross-validation, and prediction sets across three different saffron packaging methods. These findings provide further evidence for the validity and efficacy of the proposed method.

Detection of botanical adulterants in saffron powder

Saffron is a unique spice obtained by drying stigmas of saffron flowers (Crocus sativus L.). Due to its high price, economically motivated adulteration occurs relatively often. The presented study aimed to develop an effective strategy for the detection of the following potential botanical adulterants used for a saffron substitution or dilution: safflower (Carthamus tinctorius L.), calendula (Calendula officinalis L.), turmeric (Curcuma longa L.), achiote (Bixa orellana L.), red pepper (Capsicum spp.), mountain arnica (Arnica montana L.), beet (Beta vulgaris L.), and pomegranate (Punica granatum L.). A non-target screening strategy based on ultra-high performance reverse-phase liquid chromatography coupled to tandem high-resolution mass spectrometry (UHPLC-HRMS/MS) was employed for the analysis of an aqueous ethanol plant extract. By using multivariate statistical methods, principal components analysis (PCA), and partial least squares discriminant analysis (PLS-DA), for processing the generated “chemical fingerprints,” metabolites unique to the investigated plants could be identified. To enable routine saffron authenticity control by target screening, an internal spectral database was developed; currently, it involves 82 unique markers. In this way, the detection addition as low as 1% (w/w) of all analyzed botanical adulterants in admixture with saffron was possible. The developed method was used to control 7 saffron powder samples from the Czech market, and none of the monitored adulterants were confirmed.

Laser Sensing and Chemometric Analysis for Rapid Detection of Oregano Fraud.

World health is increasingly threatened by the growing number of spice-related food hazards. Further development of reliable methods for rapid, non-targeted identification of counterfeit ingredients within the supply chain is needed. ENEA has developed a portable, user-friendly photoacoustic laser system for food fraud detection, based on a quantum cascade laser and multivariate calibration. Following a study on the authenticity of saffron, the instrument was challenged with a more elusive adulterant, olive leaves in oregano. The results show that the reported method of laser sensing and chemometric analysis was able to detect adulterants at mass ratios of at least 20% in less than five minutes.

Non-targeted metabolomics and chemometrics for saffron (Crocus sativus L.) authentication and adulteration detection in relation to its anticholinesterase activity

Saffron (Crocus sativus L.), is a spice of vulnerable medical importance. It is often adulterated to lower its quality and efficacy. The present study sought to detect the level of adulteration in five traded saffron collected from different localities using a high-performance liquid chromatography coupled with high resolution tandem mass spectrometry (UPLC-HR-MS/MS) technique. In total, 62 metabolites were identified in the five extracts namely from monoterpene aldehydes, flavonoid derivatives, apocarotenoids and carotenoids classes as well as chemical markers of adulteration. One common adulterant; Gardenia jasminoides Ellis was detected through the simultaneous determination of its specific constituents including geniposide, shanzhiside and major jasminosides. Also, one synthetic dye; auramine-O was detected in one marketed saffron product. The anticholinesterase activity of the five marketed saffron was also evaluated. Furthermore, saffron samples that contain high crocin levels possessed a promising anticholinesterase activity. It was detected for the first time that auramine-O dye is sometimes used as a potential adulterant of saffron. In spite of the grand researches on saffron, it still lacks an accurate evaluation of the commercial saffron products. The results of our study showed that high adulteration rate of trade saffron not only reduces saffron efficacy as a herbal remedy but may also simultaneously represent a health risk.

A Novel FTIR-Based Chemometric Solution for the Assessment of Saffron Adulteration with Non-Fresh Stigmas.

The development of fast, non-destructive, and green methods with adequate sensitivity for saffron authentication has important implications in the quality control of the entire production chain of this precious spice. In this context, the highly suitable sensitivity of a spectroscopic method coupled with chemometrics was verified. A total number of 334 samples were analyzed using attenuated-total-reflectance Fourier-transform infrared (ATR-FTIR) spectroscopy; the collected spectra were processed by partial-least-squares discriminant analysis (PLS-DA) to evaluate the feasibility of this study for the discrimination between compliant saffron (fresh samples produced in 2020) and saffron samples adulterated with non-fresh stigmas produced in 2018 and 2016. PLS-DA was able to classify the saffron samples in accordance with the aging time and to discriminate fresh samples from the samples adulterated with non-fresh (legally expired) stigmas, achieving 100% of both sensitivity and specificity in external prediction. Moreover, PLS regression was able to predict the adulteration level with sufficient accuracy (the root-mean-square error of prediction was approximately 3-5%). In summary, ATR-FTIR and chemometrics can be employed to highlight the illegal blending of fresh saffron with unsold stocks of expired saffron, which may be a common fraudulent practice not yet considered in the scientific literature.

Volatile fingerprints of different parts of Chongming saffron (Crocus sativus) flowers by headspace-gas chromatography-ion mobility spectrometry and in vitro bioactive properties of the saffron tepals.

In this work, the volatile fingerprints of different parts of Chongming saffron flowers (stigmas, stamens, and tepals) were analyzed and compared for the first time by headspace-gas chromatography-ion mobility spectrometry. Three different parts of saffron flowers could be clearly distinguished using principal component analysis based on signal intensity data of gas chromatography-ion mobility spectrometry. Therefore, gas chromatography-ion mobility spectrometry coupled with principal component analysis method could be employed as a new method for authentication and quality control of saffron for the reason of frequent addition with stamens and/or tepals as adulterants in saffron. Moreover, the bioactive composition (total flavonoids, total phenolics, and total anthocyanins) and bioactive properties of saffron tepals were evaluated. The results indicated that aqueous, ethanol, and ethyl acetate extracts of saffron tepals exhibited good radical scavenging (2,2-Diphenyl-1-picrylhydrazyl, ABTS, and OH) and enzyme (α-amylase/α-glucosidase) inhibition activities, which probably were attributed to the bioactive components contained in the extracts. This approach would provide the important information for monitoring the quality of saffron as well as exploring the utilization of saffron tepals in functional food technology. PRACTICAL APPLICATION: This study demonstrated that the HS-GC-IMS method might be used as a new strategy for quality control of saffron, and the saffron tepals were rich source of bioactive components that could be used in health-promoting products.

The menace of saffron adulteration: Low-cost rapid identification of fake look-alike saffron using Foldscope and machine learning technology.

Saffron authenticity is important for the saffron industry, consumers, food industry, and regulatory agencies. Herein we describe a combo of two novel methods to distinguish genuine saffron from fake in a user-friendly manner and without sophisticated instruments. A smartphone coupled with Foldscope was used to visualize characteristic features and distinguish “genuine” saffron from “fake.” Furthermore, destaining and staining agents were used to study the staining patterns. Toluidine blue staining pattern was distinct and easier to use as it stained the papillae and the margins deep purple, while its stain is lighter yellowish green toward the central axis. Further to automate the process, we tested and compared different machine learning-based classification approaches for performing the automated saffron classification into genuine or fake. We demonstrated that the deep learning-based models are efficient in learning the morphological features and classifying samples as either fake or genuine, making it much easier for end-users. This approach performed much better than conventional machine learning approaches (random forest and SVM), and the model achieved an accuracy of 99.5% and a precision of 99.3% on the test dataset. The process has increased the robustness and reliability of authenticating saffron samples. This is the first study that describes a customer-centric frugal science-based approach to creating an automated app to detect adulteration. Furthermore, a survey was conducted to assess saffron adulteration and quality. It revealed that only 40% of samples belonged to ISO Category I, while the average adulteration percentage in the remaining samples was 36.25%. After discarding the adulterants from crude samples, their quality parameters improved significantly, elevating these from ISO category III to Category II. Conversely, it also means that Categories II and III saffron are more prone to and favored for adulteration by fraudsters.

Vis-NIR hyperspectral imaging coupled with independent component analysis for saffron authentication

In the present contribution, visible-near infrared hyperspectral imaging (Vis-NIR-HSI) combined with a novel chemometric approach based on mean-filed independent component analysis (MF-ICA) followed by multivariate classification techniques is proposed for saffron authentication and adulteration detection. First, MF-ICA was used to exploit pure spatial and spectral profiles of the components. Then, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used to find patterns of authentic samples based on their distribution maps. Then, detection of five common plant-derived adulterants of saffron including safflower, saffron style, calendula, rubia and turmeric were investigated. For this purpose, partial least squares-discriminant analysis (PLS-DA) for supervised classification to find a boundary between authentic and adulterated saffron samples. Classification accuracies for all models for calibration and prediction sets were 100 %. Finally, a mixed dataset was prepared and analyzed using the proposed strategy which again 100 % of accuracies for calibration and prediction sets were obtained. At the end, data driven soft independent modelling of class analogy (dd-SIMCA) was used to evaluate model for class modeling. Sensitivity was 95% for authentic class and specificities for all adulterants were 100%.

Non-Targeted NMR Method to Assess the Authenticity of Saffron and Trace the Agronomic Practices Applied for Its Production

The development of analytical methods aimed at tracing agri-food products and assessing their authenticity is essential to protect food commercial value and human health. An NMR-based non-targeted method is applied here to establish the authenticity of saffron samples. Specifically, 40 authentic saffron samples were compared with 18 samples intentionally adulterated by using turmeric and safflower at three different concentration levels, i.e., 5, 10, and 20 wt%. Statistical processing of NMR data furnished useful information about the main biomarkers contained in aqueous and dimethyl sulfoxide extracts, which are indicative of the presence of adulterants within the analyzed matrix. Furthermore, a discrimination model was developed capable of revealing the type of agronomic practice adopted during the production of this precious spice, distinguishing between organic and conventional cultivation. The main objective of this work was to provide the scientific community involved in the quality control of agri-food products with an analytical methodology able to extract useful information quickly and reliably for traceability and authenticity purposes. The proposed methodology turned out to be sensitive to minor variations in the metabolic composition of saffron that occur in the presence of the two adulterants studied. Both adulterants can be detected in aqueous extracts at a concentration of 5 wt%. A lower limit of detection was observed for safflower contained in organic extracts in which case the lowest detectable concentration was 20%.

Optimized Isolation of Safranal from Saffron by Solid-Phase Microextraction (SPME) and Rotatable Central Composite Design-Response Surface Methodology (RCCD-RSM)

Safranal is the main aroma component of saffron stigmas. It is also a great antioxidant with known pharmacological properties and is a potent indicator for the grading and authentication of saffron. In this study, the optimum extraction conditions of safranal from saffron stigmas were investigated using solid-phase microextraction-gas chromatography-mass spectrometry (SPME-GC-MS) and response surface methodology (RSM). A rotatable-central composite design was applied, and a linear regression model has been used for the model building. The optimized factors were as follows: sample weight (15 mg), water volume (4 mL), exposure time in the headspace (20 min), and extraction temperature (45 °C). All factors were found significant; however, extraction temperature and exposure time were the most important for the isolation of safranal. The obtained model was successfully validated with a test set of saffron samples analyzed under the optimum extraction conditions. The optimized SPME extraction conditions of safranal found in this study contribute to the efforts towards the detection of saffron authentication and adulteration.

RNA-seq based transcriptome analysis of ethanol extract of saffron protective effect against corticosterone-induced PC12 cell injury

BackgroundAt present, oral antidepressants are commonly used in the clinical treatment of depression. However, the current drug treatment may lead to more serious adverse reactions. Therefore, we focus on Chinese traditional medicine, trying to find an effective and safe alternative or complementary medicine. Crocus sativus (saffron) is a traditional Chinese herbal medicine, which is typically used in the clinic to regulate anxiety, insomnia, amnesia, and other mental disorder. The study aimed to explore the neuroprotective effect of ethanol extract of saffron (EES) on corticosterone (CORT)- induced injury in PC12 cells and further explored its potential mechanism.MethodsThe authenticity of saffron and the active components of EES were identified by a water test and ultra-performance liquid chromatography-time of flight mass spectrometry system. The screening of cytotoxicity for PC12 cells was incubated with EES in different concentrations for 24 h, and the protective efficacy of EES on CORT (500 μM) -induced PC12 cell injury, cell viability was assessed by Cell Counting Kit-8 (CCK-8) assay. The differentially expressed genes (DEGs) of EES-protected PC12 cells were analyzed using the RNA-seq method, and the results were analyzed for GO and KEGG enrichment. The results of RNA-seq were verified by qPCR analysis.ResultsThe saffron was initially identified as authentic in the water test and 10 compounds were identified by Ultra Performance Liquid Chromatography (UPLC)- Mass Spectrometry (MS). The results of CCK-8 demonstrated that EES at concentrations above 640 μg/mL exerted a certain cytotoxic effect, and PC12 cells pretreated with EES (20, 40, and 80 μg/mL) significantly reversed the 500 μM CORT-induced cell death. RNA-seq analysis showed that EES regulated 246 differential genes, which were mainly enriched in the MAPK signaling pathway. Dusp5, Dusp6, Gadd45b, Gadd45G, and Pdgfc were further validated by qPCR. Experimental data showed that the results of qPCR were consistent with RNA-seq.ConclusionsThese findings provide an innovative understanding of the molecular mechanism of the protective effect of EES on PC12 cells at the molecular transcription level, and Dusp5, Dusp6, Gadd45b, Gadd45g, and Pdgfc may be potential novel targets for antidepressant treatment.

Chemometrics-assisted isotope ratio fingerprinting based on gas chromatography/combustion/isotope ratio mass spectrometry for saffron authentication

In the present contribution, the capability of isotopic ratio mass spectrometry (IRMS) for saffron authentication and detection of four common plant-derived adulterants (marigold flower, safflower, rubia, and saffron style) was investigated. For this purpose, 62 authentic saffron samples were analyzed by elemental analyzer-IRMS (EA-IRMS) and gas chromatography-combustion-IRMS (GC-C-IRMS). In this regard, EA-IRMS and GC-C-IRMS isotope fingerprints of carbon-13 and nitrogen-15 isotopes of saffron components were provided and then analyzed by chemometric methods. Principal component analysis (PCA) showed two different behaviors regarding two main regions. Then, a representative saffron sample was provided to study adulteration. On this matter, binary mixtures of saffron and adulterants were prepared at five different weight percentages (5%, 10%, 15%, 25%, and 35%) and analyzed by EA-IRMS and GC-C-IRMS. Data-driven soft independent modeling of class analogy (DD-SIMCA) was used to model authentic saffron samples and find a boundary between authentic and adulterated samples with a sensitivity of 100% by GC-C-IRMS. After that, discriminant models of linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and partial least squares-discriminant analysis (PLS-DA) were tested to find the best discrimination line and also detection of the lowest level of adulterants. Among different models, the QDA model outperformed other methods and showed the ability to predict adulterants at 5% w/w level with 100% accuracy and precision. Finally, the developed QDA model was successfully used to discriminate a set of mixed samples of saffron and four adulterants as well as some commercial samples.