Multivariate screening in food adulteration: Untargeted versus targeted modelling

The variability of phenolic constituents and antioxidant properties among wild populations of Ziziphora clinopodioides Lam

In this study, phenolic derivatives and antioxidant activities of fourteen Ziziphora clinopodioides populations, as well as LC-MS/MS analysis of three specific flavonoids were evaluated. Generally, high contents of phenolic derivatives were found in shoot extracts compared to roots. LC-MS/MS, a powerful analytical technique, was employed for the identification and quantify the individual flavonoids in Z. clinopodioides populations' extracts, in a quantity order of quercetin > rutin > apigenin. Scavenging activity by DPPH and FRAP was performed, and accordingly, in the shoot, the highest values for the DDPH were 4.61 ± 0.4 and 7.59 ± 0.26 µg ml- 1 in populations 1 and 13, respectively, and for the FRAP were 328.61 ± 5.54 and 292.84 ± 2.85 mg g DW- 1, in populations 6 and 1 respectively. Multivariate analysis results of the principal component analysis indicated the amount of polyphenols to be useful indicators in differentiating the geographical localities which explain 92.7% of the total variance. According to the results of hierarchical cluster analysis, the studied populations could be separated into two groups in that the contents of phenolic derivatives and antioxidant activities of different plant parts. Both shoot and root samples were well discriminated with the orthogonal partial least squares discriminant analysis (R2X: 0.861; Q2: 0.47) model. The validity of the model was confirmed by using receiver operating characteristic curve analysis and permutation tests. Such data make an important addition to our current knowledge of Ziziphora chemistry and are decisive in the identification of germplasms with a homogeneous phytochemical profile, high chemical content and bioactivity. The present results could also be helpful for the potential application of Z. clinopodioides in different kinds of industries as natural antioxidants.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12298-023-01283-y.

One-class model with two decision thresholds for the rapid detection of cashew nuts adulteration by other nuts

A green screening method to determine cashew nut adulteration with Brazilian nut, pecan nut, macadamia nut and peanut was proposed. The method was based on the development of a one-class soft independent modelling of class analogy (SIMCA) model for non-adulterated cashew nuts using near-infrared (NIR) spectra obtained with portable equipment. Once the model is established, the assignment of unknown samples depends on the threshold established for the authentic class, which is a key aspect in any screening approach. The authors propose innovatively to define two thresholds: lower model distance limit and upper model distance limit. Samples with distances below the lower threshold are assigned as non-adulterated with a 100% probability; samples with distance values greater than the upper threshold are assigned as adulterated with a 100% probability; and samples with distances within these two thresholds will be considered uncertain and should be submitted to a confirmatory analysis. Thus, the possibility of error in the sample assignment significantly decreases. In the present study, when just one threshold was defined, values greater than 95% for the optimized threshold were obtained for both selectivity and specificity. When two class thresholds were defined, the percentage of samples with uncertain assignment changes according to the adulterant considered, highlighting the case of peanuts, in which 0% of uncertain samples was obtained. Considering all adulterants, the number of samples that were submitted to a confirmatory analysis was quite low, 5 of 224 adulterated samples and 3 of 56 non-adulterated samples.

Adulteration Detection of Edible Bird’s Nests Using Rapid Spectroscopic Techniques Coupled with Multi-Class Discriminant Analysis

Edible bird’s nests (EBNs) are vulnerable to adulteration due to their huge demand for traditional medicine and high market price. Presently, there are pressing needs to explore field-deployable rapid screening techniques to detect adulteration of EBNs. The objective of this study is to explore the feasibility of using a handheld near-infrared (VIS/SW-NIR) spectroscopic device for the determination of EBN authenticity against the benchmark performance of a benchtop mid-infrared (MIR) spectrometer. Forty-nine authentic EBNs from the different states in Malaysia and 13 different adulterants (five types) were obtained and used to simulate the adulteration of EBNs at 1, 5 and 10% adulteration by mass (a total of 15 adulterated samples). The VIS/SW-NIR and MIR spectra collated were subsequently processed, modelled and classified using multi-class discriminant analysis. The VIS/SW-NIR results showed 100% correct classification for the collagen and nutrient agar classes in authenticity classification, while for the other classes, the lowest correct classification rate was 96.3%. For MIR analysis, only the karaya gum class had 100% correct classification whilst for the other four classes, the lowest rate of correct classification was at 94.4%. In conclusion, the combination of spectroscopic analysis with chemometrics can be a powerful screening tool to detect EBN adulteration.

Rapid detection of carmine in black tea with spectrophotometry coupled predictive modelling

Carmine is an artificial colorant commonly used by fraudulent food business participants in black tea adulteration, for purpose of gaining illegal profits. This study combined spectrophotometry with machine learning for rapid detection of carmine in black tea based on the spectral characteristics of tea infusion. The qualitative model demonstrated an accuracy rate of 100% for successful identification of the presence/absence of carmine in black tea. For quantitative analysis, the R² between carmine concentrations generated according to spectral characteristics and those determined with HPLC was 0.988 and 0.972, respectively, for black tea samples involved in the test subset and an independent dataset II. Paired t-test indicated that the difference was statistically insignificant (P values of 0.26 and 0.44, respectively). The method established in this study was rapid and reliable for detecting carmine in black tea, and thus could be used as a useful tool to identify black tea adulteration in market.

ROC curves for the optimization of one-class model parameters. A case study: Authenticating extra virgin olive oil from a Catalan protected designation of origin

This paper proposes a ROC curve-based methodology to find optimal classification model parameters. ROC curves are implemented to set the optimal number of PCs to build a one-class SIMCA model and to set the threshold class value that optimizes both the sensitivity and specificity of the model. The authentication of the geographical origin of extra-virgin olive oils of Arbequina botanical variety is presented. The model was developed for samples from Les Garrigues, target class, Samples from Siurana were used as the non-target class. Samples were measured by FT-Raman with no pretreatment. PCA was used as exploratory technique. Spectra underwent pre-treatment and variables were selected based on their VIP score values. ROC curve and others already known criteria were applied to set the threshold class value. The results were better when the ROC curve was used, obtaining performance values higher than 82%, 75% and 77% for sensitivity, specificity and efficiency, respectively.

Detection of adulteration in food based on nondestructive analysis techniques: a review

In recent years, people pay more and more attention to food quality and safety, which are significantly relating to human health. Food adulteration is a world-wide concerned issue relating to food quality and safety, and it is difficult to be detected. Modern detection techniques (high performance liquid chromatography, gas chromatography-mass spectrometer, etc.) can accurately identify the types and concentrations of adulterants in different food types. However, the characteristics as expensive, low efficient and complex sample preparation and operation limit the use of these techniques. The rapid, nondestructive and accurate detection techniques of food adulteration is of great and urgent demand. This paper introduced the principles, advantages and disadvantages of the nondestructive analysis techniques and reviewed the applications of these techniques in food adulteration screen in recent years. Differences among these techniques, differences on data interpretation and future prospects were also discussed.

Chemometric tools for food fraud detection: The role of target class in non-targeted analysis

The chemometric issues related to the application of non-targeted analysis for the detection of food frauds were analyzed employing discriminant analysis and a one-class classifier. The similarities and differences between the two methods were investigated. The results of classification are characterized by a set of indices called figures of merit. They comprehensively characterized the quality and reliability of classification. The principle is illustrated using an actual example of Oregano herbs adulteration. The informative region 9000-4000 cm^-1 of near-Infrared spectroscopy is used as analytical means. The results of the application of each method for Oregano data collection are presented. It is shown that the discriminant method is only partially appropriate for solving the authentication problem. One class classifier is a powerful and devoted for non-targeted analysis. The step by step analysis introduced in the paper can also be successfully utilized in apply for revealing of forgeries of various food products.

Automatic soft independent modeling for class analogies

Soft independent modeling of class analogy (SIMCA) is an important method for authentication. The key parameters for SIMCA, the number of principal components and the decision threshold, determine the model's performance. In this report, a self-optimizing SIMCA that automatically determines these two parameters is devised and referred to as automatic SIMCA (aSIMCA). An efficient optimization is obtained by incorporating response surface modeling (RSM) and bootstrapped Latin partitions with the model-building dataset. A set of design points over the ranges of the two parameters are evaluated with respect to sensitivity and specificity by using the model-building data from target and non-target classes. Averages of the sensitivity and specificity are used as responses for the design points. A 2-dimensional interpolation and a bivariate cubic polynomial were used to model the response surface. As a control method, a grid search that evaluates all combinations of the two parameters over the same ranges was performed in parallel to determine the best conditions for SIMCA and the modeling performance was compared to aSIMCA with RSM. The developed aSIMCA methods were evaluated by authenticating two botanical extracts sets, i.e., marijuana and hemp, with spectral datasets collected from various spectroscopic techniques, including nuclear magnetic resonance, high-resolution mass, and ultraviolet spectrometry. Results of a paired t-test indicated that the aSIMCA with the RSM had similar performance with the one optimized by the grid search for modeling marijuana and hemp, while the RSM was more computationally efficient. The 2-dimensional interpolation is preferred because the better efficiency and the fit to the response surface is more precise.

Nuclear Magnetic Resonance (NMR) Spectroscopy in Food Science: A Comprehensive Review

Nuclear magnetic resonance (NMR) spectroscopy is a robust method, which can rapidly analyze mixtures at the molecular level without requiring separation and/or purification steps, making it ideal for applications in food science. Despite its increasing popularity among food scientists, NMR is still an underutilized methodology in this area, mainly due to its high cost, relatively low sensitivity, and the lack of NMR expertise by many food scientists. The aim of this review is to help bridge the knowledge gap that may exist when attempting to apply NMR methodologies to the field of food science. We begin by covering the basic principles required to apply NMR to the study of foods and nutrients. A description of the discipline of chemometrics is provided, as the combination of NMR with multivariate statistical analysis is a powerful approach for addressing modern challenges in food science. Furthermore, a comprehensive overview of recent and key applications in the areas of compositional analysis, food authentication, quality control, and human nutrition is provided. In addition to standard NMR techniques, more sophisticated NMR applications are also presented, although limitations, gaps, and potentials are discussed. We hope this review will help scientists gain some of the knowledge required to apply the powerful methodology of NMR to the rich and diverse field of food science.

A review on the application of vibrational spectroscopy to the chemistry of nuts

Nuts are highly appreciated due to their nutritional relevance and flavour, being the source of many desirable and healthy compounds as polyunsaturated fatty acids and antioxidants. Their characterization became the target of many studies in the last years through conventional analytical techniques as chromatographic ones. Due to the limitations associated to these techniques, as time, cost and environmental concerns, spectroscopic techniques have been increasingly pointed as reliable alternatives. Either applied to raw materials quality control or to more complex process, as industrial in-line monitoring, spectroscopic techniques, namely vibrational spectroscopy, are gathering strong acceptance. This paper presents a review on the application of vibrational spectroscopy, infrared and Raman, to nuts characterization. Estimates of several qualitative and quantitative parameters, origin authentication and/or adulteration in almonds, peanuts, pine nuts, hazelnuts, walnuts, Brazil nuts, cashews, chestnuts and pistachios will be covered. Advantages and limitations of these techniques and future trends will also be discussed.

Comparative appraisal of ghee and common vegetable oils for spectral characteristics in FT-MIR reflectance spectroscopy

FT-MIR spectra of ghee (anhydrous milk fat) and common vegetable oils were acquired using HATR in 4000-650 cm^-1 region. The differences in absorbance by carbon-hydrogen (C-H) stretch in fatty acid chain at 3.48 μm and absorbance by carbonyl (C-O) stretch of ester linkage at 5.7 μm in ghee and that in vegetable oils were studied. The clear differences in the spectra of ghee and that of the vegetable oils were noticed in fingerprint region, which can be very well utilized to develop FT-MIR spectroscopy as a promising tool to detect presence of common vegetable oils mixed in the ghee as an adulterant.

Detection of adulterants in grape nectars by attenuated total reflectance Fourier-transform mid-infrared spectroscopy and multivariate classification strategies

There is no any doubt about the importance of food fraud control, as it has implications in food safety and in consumer health. Focusing on fruit beverages, some types of adulterations have been detected more frequently, such as substitution with less expensive fruits. A methodology based on attenuated total reflectance Fourier-transform mid-infrared spectroscopy (ATR-FTIR) and multivariate classification was applied to detect whether grape nectars were adulterated by substitution with apple juice or cashew juice. A total of 126 samples were obtained and analyzed. Two strategies were proposed: one-class and multiclass approaches. Soft independent modeling of class analogy (SIMCA), partial least squares discriminant analysis (PLS-DA) and partial least squares density modeling (PLS-DM) were used to build the models. Among them, PLS-DA presented the best performance with a sensitivity and specificity of nearly 100%. The multiclass strategy was preferred if the adulterants to be studied are known because it provides additional information.

Overview on Untargeted Methods to Combat Food Frauds: A Focus on Fishery Products

Authenticity and traceability of food products are of primary importance at all levels of the production process, from raw materials to finished products. Authentication is also a key aspect for accurate labeling of food, which is required to help consumers in selecting appropriate types of food products. With the aim of guaranteeing the authenticity of foods, various methodological approaches have been devised over the past years, mainly based on either targeted or untargeted analyses. In this review, a brief overview of current analytical methods tailored to authenticity studies, with special regard to fishery products, is provided. Focus is placed on untargeted methods that are attracting the interest of the analytical community thanks to their rapidity and high throughput; such methods enable a fast collection of “fingerprinting signals” referred to each authentic food, subsequently stored into large database for the construction of specific information repositories. In the present case, methods capable of detecting fish adulteration/substitution and involving sensory, physicochemical, DNA-based, chromatographic, and spectroscopic measurements, combined with chemometric tools, are illustrated and commented on.