Saffron Adulteration Research Articles

Rapid and low-cost detection of saffron (Crocus sativus L.) adulteration using smartphone videos and spectral data fusion strategy

Saffron (Crocus sativus L.) is the most expensive spice in the world and is highly susceptible to economically motivated adulteration. Hyperspectral imaging (HSI) and near-infrared (NIR) spectroscopy are recently popular techniques for rapid, non-destructive detection of saffron adulteration. However, the high cost and measurement complexity of these techniques make them less suitable for consumer-level applications. In this work, a smartphone video-based system combined with chemometrics is used to detect saffron adulteration. A smartphone is utilised to capture short videos of pure and adulterated saffron powder samples illuminated by its colour-changing screen. The video frames are decomposed into RGB colour images and converted into spectral-like data. Partial least squares regression is used to model the relationship between data and saffron purity. The smartphone video-based system obtains a coefficient of determination of 0.9774 for prediction, which is comparable to reference techniques such as NIR spectroscopy and HSI. Moreover, it is efficient in presenting distribution maps related to saffron purity. In addition, when used in conjunction with the smartphone video-based system, HSI and NIR spectroscopy achieve higher performance without significantly increasing measurement cost and complexity. Such results suggest that the smartphone video-based system has the potential to be a viable primary screening and auxiliary tool for detecting saffron adulteration.

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.

A novel 3D-fluorescence sensing strategy based on HN-chitosan polymer probe for rapid identification and quantification of potential adulteration in saffron.

Saffron is a candidate for various kinds of fraud to make huge profits. The present study proposed an efficient three-dimensional (3D) fluorescence sensing strategy based on hydrophilic hydrazine-naphthalimide functionalized chitosan (HN-chitosan) polymer probe for rapid identification and quantification of potential adulteration in saffron. The amino functional group in the HN-chitosan probe reacted specifically with the Oxygen-containing group of active ingredients in saffron, amplifying the signal difference between saffron and the adulterants, which was comprehensively characterized by 3D fluorescence. Four advanced chemometrics methods were applied for the classification of saffron and adulterated saffron, and good performance were obtained in both training and prediction sets. Furthermore, the PLS regression model was applied to the prediction of adulteration level in saffron and showed satisfactory accuracy. This strategy provides a new solution for rapid identification and quantification of potential adulteration in saffron, which contributes to the healthy development of its industry.

Identification of adulteration in the market samples of saffron using morphology, HPLC, HPTLC, and DNA barcoding methods.

Saffron, the stigma of Crocus sativus L., is the most expensive spice used for culinary, medicinal, dye, and cosmetics purposes. It is highly adulterated because of its limited production and high commercial value. In this study, 104 saffron market samples collected from 16 countries were tested using morphology, high-performance liquid chromatography (HPLC), high-performance thin-layer chromatography (HPTLC), and deoxyribonucleic acid (DNA) barcoding. Overall, 45 samples (43%) were adulterated. DNA barcoding identified the highest number of adulterated saffron (44 samples), followed by HPTLC (39 samples), HPLC (38 samples), and morphology (32 samples). Only DNA barcoding identified the adulterated samples containing saffron and other plants' parts as bulking agents. In addition, DNA barcoding identified 20 adulterant plant species, which will help develop quality control methods and market surveillance. Some of the adulterant plants are unsafe for human consumption. The HPLC method helped identify the saffron samples adulterated with synthetic safranal. HPLC and HPTLC methods will help identify the samples adulterated with other parts of the saffron plant (auto-adulteration).

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.

Rapid detection and quantification of adulteration in saffron by excitation-emission matrix fluorescence combined with multi-way chemometrics.

Saffron has gained people's attention and love for its unique flavor and valuable edible value, but the problem of saffron adulteration in the market is serious. It is urgent for us to find a simple and rapid identification and quantitative estimation of adulteration in saffron. Therefore, excitation-emission matrix (EEM) fluorescence combined with multi-way chemometrics was proposed for the detection and quantification of adulteration in saffron. The fluorescence composition analysis of saffron and saffron adulterants (safflower, marigold and madder) were accomplished by alternating trilinear decomposition (ATLD) algorithm. ATLD and two-dimensional principal component analysis combined with k-nearest neighbor (ATLD-kNN and 2DPCA-kNN) and ATLD combined with data-driven soft independent modeling of class analogies (ATLD-DD-SIMCA) were applied to rapid detection of adulteration in saffron. 2DPCA-kNN and ATLD-DD-SIMCA methods were adopted for the classification of chemical EEM data, first with 100% correct classification rate. The content of adulteration of adulterated saffron was predicted by the N-way partial least squares regression (N-PLS) algorithm. In addition, new samples were correctly classified and the adulteration level in adulterated saffron was estimated semi-quantitatively, which verifies the reliability of these models. ATLD-DD-SIMCA and 2DPCA-kNN are recommended methods for the classification of pure saffron and adulterated saffron. The N-PLS algorithm shows potential in prediction of adulteration levels. These methods are expected to solve more complex problems in food authenticity. © 2023 Society of Chemical Industry.

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.

Improving the Accuracy of Saffron Adulteration Classification and Quantification through Data Fusion of Thin-Layer Chromatography Imaging and Raman Spectral Analysis.

Agricultural crops of high value are frequently targeted by economic adulteration across the world. Saffron powder, being one of the most expensive spices and colorants on the market, is particularly vulnerable to adulteration with extraneous plant materials or synthetic colorants. However, the current international standard method has several drawbacks, such as being vulnerable to yellow artificial colorant adulteration and requiring tedious laboratory measuring procedures. To address these challenges, we previously developed a portable and versatile method for determining saffron quality using a thin-layer chromatography technique coupled with Raman spectroscopy (TLC-Raman). In this study, our aim was to improve the accuracy of the classification and quantification of adulterants in saffron by utilizing mid-level data fusion of TLC imaging and Raman spectral data. In summary, the featured imaging data and featured Raman data were concatenated into one data matrix. The classification and quantification results of saffron adulterants were compared between the fused data and the analysis based on each individual dataset. The best classification result was obtained from the partial least squares-discriminant analysis (PLS-DA) model developed using the mid-level fusion dataset, which accurately determined saffron with artificial adulterants (red 40 or yellow 5 at 2-10%, w/w) and natural plant adulterants (safflower and turmeric at 20-100%, w/w) with an overall accuracy of 99.52% and 99.20% in the training and validation group, respectively. Regarding quantification analysis, the PLS models built with the fused data block demonstrated improved quantification performance in terms of R2 and root-mean-square errors for most of the PLS models. In conclusion, the present study highlighted the significant potential of fusing TLC imaging data and Raman spectral data to improve saffron classification and quantification accuracy via the mid-level data fusion, which will facilitate rapid and accurate decision-making on site.

Phytochemistry, quality control and medicinal uses of Saffron (Crocus sativus L.): an updated review.

Saffron, botanically known as Crocus sativus L., is renowned as the world's most expensive spice and has been utilized in various fields since ancient times. Extensive scientific research has been conducted on Crocus sativus (C. sativus), focusing on its phytochemical composition, diverse applications, and biological activities. C. sativus phytochemicals consist mainly of three compounds, namely crocin, picrocrocin, and safranal, which are responsible for most of its properties. Saffron is rich in bioactive compounds, more than 150 of which have been isolated. Owing to its unique composition and properties, saffron is used in various fields, such as the food industry, perfumery, cosmetics, pharmaceutics, and medicine. However, the high economic value of saffron makes it susceptible to adulteration and various fraudulent practices. To deal with this issue, a number of methods and techniques have been developed to authenticate and determine adulterants in saffron. This paper presents a bibliometric study of saffron based on the Web of Science database, analyzing 3,735 studies published between 2000 and 2021. The study also examined author participation and collaboration networks among countries. Production, transformation, chemical composition, methods of adulteration detection, uses, and health properties of saffron are also discussed.

Mirror-Stamping: A new TLC-SERS method for color authentication

Adulteration of natural colorants with artificial ones compromises the authenticity, quality, and safety of food products. It is significant for food companies to check the raw colorant ingredients to avoid potential adulteration. Combining thin-layer chromatography (TLC) and surface-enhanced Raman spectroscopy (SERS) is a promising colorant separation and detection approach. Herein, we developed a novel and simple TLC-SERS approach based on silver mirror-stamping, which was achieved by pressing a layer of aggregated silver nanoparticles on top of the target analytes on the developed TLC chip. The performance of the stamping method was evaluated on the separation and signal enhancement of an adulterated saffron model matrix (saffron and red 40) as compared to the other two methods (mirror-first and mirror-last methods). The results demonstrated that the mirror-stamping method achieved optimal separation performance with strong SERS signals. This can be attributed to the reduced sample disruption and dilution. Therefore, the mirror-stamping approach demonstrates great potential for use in TLC-SERS analysis to rapidly and accurately determine natural colorant authenticity and quality.

Smart Framework for Quality Check and Determination of Adulterants in Saffron Using Sensors and AquaCrop

Saffron is a rare and valuable crop that is only cultivated in specific regions with suitable topographical conditions. To improve saffron cultivation, it is crucial to monitor and precisely control the crop’s agronomic variables over at least one growth cycle to create a fully automated environment. To this end, agronomic variables in the Punjab region of India were analyzed and set points were calculated using third-order polynomial equations through the application of image processing techniques. The relationship between canopy cover, growth percentage, and agronomic variables was also investigated for optimal yield and quality. The addition of adulterants, such as turmeric and artificial colorants, to saffron is a major concern due to the potential for quality compromise and fraud by supply chain vendors. Hence, there is a need for devising an easy, reliable, and user-friendly mechanism to help in the detection of adulterants added to the saffron stigmas. This paper proposes an automated IoT-based saffron cultivation environment using sensors for determining set points of agronomical variables. In addition, a sensor-based chamber has been proposed to provide quality and adulteration checks of saffron and to eliminate product counterfeiting. The AquaCrop simulator was employed to evaluate the proposed framework’s performance. The results of the simulation show improved biomass, yield, and harvest index compared with the existing solutions in precision agriculture. Given the high value and demand for saffron, ensuring its purity and quality is essential to sustain its cultivation and the economic viability of the market.

SaffNet: an ensemble-based approach for saffron adulteration prediction using statistical image features.

Saffron is one of the costlier spices that are cultivated in specific regions of the world. Due to its restricted accessibility and more popularity, eventually saffron adulteration is one of the concerning issues in the recent times. It becomes difficult for human vision to discriminate between real and adulterated saffron samples. With the emergence of visual computing and data-driven algorithms, the saffron adulteration prediction systems (SAPS) are designed to predict the original and adulterated saffron samples. However, the majority of the techniques exhibit promising performance but the problem of generalization capabilities (unseen - samples) and scarcity of the saffron databases are still open research challenges. In this work, to overcome these issues, we propose a novel ensemble-based saffron prediction model (SaffNet) using statistical image features for the detection of contamination in the Kashmiri saffron. As data-driven approaches mainly rely on the representative samples, but to the best of our knowledge the standard benchmark datasets for Kashmiri saffron is not available. Therefore, we have created our novel Saffron dataset (Saff-Kash) collected afresh from different parts of Kashmir valley that includes the samples of both the authentic and adulterated saffron classes. The primary aim of the work is to anticipate the adulteration in saffron samples. Thereafter, these images are pre-processed and the dataset is prepared for the proposed SaffNet model. The SaffNet architecture designed using gradient boosting ensemble evaluated on Saff-Kash outperforms the outcomes of individual classifiers i.e., Support vector machine (SVM), decision tree, and K-Nearest neighbor (KNN) with an overall accuracy of 98%. Moreover, the execution time taken by the SaffNet model for training the SVM classifier is 8.56 milliseconds whereas for gradient boosting classifier it is 7.7 milliseconds.

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.

ATR-FTIR spectroscopy combined with DNA barcoding and GC-MS to assess the quality and purity of saffron (Crocus sativus L.)

Fourier transform infrared spectra of saffron (Crocus sativus L.) samples were acquired using attenuated total reflectance (ATR-FTIR). The main objective of the study was to determine the chemical composition of 11 samples of saffron collected from different areas in Morocco using the chemometric analysis of ATR-FTIR fingerprints and identifying the adulterated saffron among samples bought from local markets in different countries (Spain, Iran, and Morocco). The the authenticity and the purity of saffron samples was validated through a molecular analysis (DNA barcoding coupled to sequencing) and chromatographic analysis GC-MS. The results of ATR-FTIR showed vibration intensities of six distinct fingerprint regions displaying statistically significant differences. The spectrum of the sample from Timjicht (Taznakht) showed typical bands due to the vibration in 3000–2800 cm-1 (the richest in carbohydrates, lipids, and amino acids) and 1800–1725 cm-1 region (the richest in carbonyl and ester groups) and was classified a single subset in samples scatter plot. Then samples from Boulmane (S2), Ain Leuh (S3), Taliouine (S6), and Taznakht (S7-S8) were classified close to each other, which indicates the similarity in their vibration intensities mainly in the region of carbohydrates, lipids, amino acids, and esters. Similarities in terms of proteins and hydroxyl groups were revealed between the samples from El Mers (S11) and Taliouine (S1). Finally, the last sub-group contained samples from Ourika, Azilal and Ain Atia, which showed low composition in all components. Furthermore, to detect adulterated saffron from samples of unknown origin, a comparison of the ATR-FTIR spectra were carried out with spectra of pure saffron and results showed that the peaks at 1706, 1732, and 1225 cm-1 (linked to crocin which are present primarily in saffron) were absent in one sample (SI). Interestingly, the use of another plant species named Arrhenatherum elatius as materiel for saffron adulteration was confirmed by the molecular study (DNA barcoding) and chromatographic analysis GC-MS

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%.

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.

DNA Accounting: Tallying Genomes to Detect Adulterated Saffron.

The EU General Food Law not only aims at ensuring food safety but also to ‘prevent fraudulent or deceptive practices; the adulteration of food; and any other practices which may mislead the consumer’. Especially the partial or complete, deliberate, and intentional substitution of valuable ingredients (e.g., Saffron) for less valuable ones is of concern. Due to the variety of products on the market an approach to detect food adulteration that works well for one species may not be easily applicable to another. Here we present a broadly applicable approach for the detection of substitution of biological materials based on digital PCR. By simultaneously measuring and forecasting the number of genome copies in a sample, fraud is detectable as a discrepancy between these two values. Apart from the choice of target gene, the procedure is identical across all species. It is scalable, rapid, and has a high dynamic range. We provide proof of concept by presenting the analysis of 141 samples of Saffron (Crocus sativus) from across the European market by DNA accounting and the verification of these results by NGS analysis.

Photoacoustic Laser System for Food Fraud Detection.

Economically motivated adulterations of food, in general, and spices, in particular, are an emerging threat to world health. Reliable techniques for the rapid screening of counterfeited ingredients in the supply chain need further development. Building on the experience gained with CO2 lasers, the Diagnostic and Metrology Laboratory of ENEA realized a compact and user-friendly photoacoustic laser system for food fraud detection, based on a quantum cascade laser. The sensor has been challenged with saffron adulteration. Multivariate data analysis tools indicated that the photoacoustic laser system was able to detect adulterants at mass ratios of 2% in less than two minutes.