Sequential Data Fusion Techniques for the Authentication of the P.G.I. Senise (“Crusco”) Bell Pepper

Abstract

Bell pepper is the common name of the berry obtained from some varieties of the Capsicum annuum species. This agro-food is appreciated all over the world and represents one of the key ingredients of several traditional dishes. It is used as a fresh product, or dried and ground as a seasoning (e.g., paprika). Specific varieties of sweet pepper present organoleptic peculiarities and they have been awarded by quality marks as a further confirmation of their unicity (e.g., Piment d’Espelette, Pimiento de Herbón, Peperone di Senise). Due to the market value of this aliment, it can be subjected to frauds, such as adulterations and sophistication. The present study lays on these considerations and aims at developing a spectroscopy-based approach for authenticating Senise bell pepper and for detecting its adulteration with common paprika. In order to achieve this goal, 60 pure samples of bell pepper from Senise were analyzed by mid- and near-infrared spectroscopies. Then, in order to mimic the adulteration, 40 mixtures of Senise bell pepper and paprika were prepared and analyzed (by the same spectroscopic techniques). Eventually, two different multi-block classification approaches (sequential and orthogonalized partial least squares linear discriminant analysis and sequential and orthogonalized covariance selection linear discriminant analysis) were used to discriminate between pure and adulterated Senise bell pepper samples. Both proposed procedures achieved extremely successful results in external validation, correctly classifying all the (thirty-five) test samples, indicating that both approaches represent a winning solution for the investigated classification problem.

Geographical Discrimination of Bell Pepper (Capsicum annuum) Spices by (HS)-SPME/GC-MS Aroma Profiling and Chemometrics

“…In agreement with the classification strategy we are proposing, the best results (in terms of correct classification rate on the external set of samples) were obtained when the classifier is PLS-DA (reaching 95% accuracy). A similar work was published by Biancolillo et al [8], which aimed at the spectroscopic characterization of the Senise bell pepper, and at determining its possible adulteration with paprika. In this case, the ground samples were analyzed by Near and Mid Infrared spectroscopies.…”

“…Unlike detection of adulterants (illegal artificial colorants or bulking agents), false origin labelling or contamination of origin-certified spices with low-quality products cultivated elsewhere cannot be unveiled by conventional targeted analytical methods due to the lack of specific markers directly related to the product origin [6]. Various fingerprinting or profiling methods, based on vibrational spectroscopies [7,8], high-or ultrahigh-performance liquid-chromatography coupled to different detector systems [9][10][11][12], and energy dispersive X-ray fluorescence [13], have been proposed to identify the origin of bell pepper spices. In this context, it has been found that the profiles of phenolic acids, polyphenolic compounds and capsacinoids are promising indicators for geographical traceability purposes.…”

Application of Spectroscopy in Food Analysis: Volume II

“…The other two works, proposed by Biancolillo et al and Le Nguyen et al, exploit IR and NIR spectroscopy for adulteration detection in agro-foods. In the former paper [11], the investigation is focused on a PGI Italian dry bell pepper sold ground and used as a seasoning. The work aims at developing a tool that determines whether a product consists of pure Senise bell pepper or has been adulterated with similar ground foods (for example, paprika).…”

Spectra Fusion of Mid-Infrared (MIR) and X-ray Fluorescence (XRF) Spectroscopy for Estimation of Selected Soil Fertility Attributes

“…While the second block was built with XRF data due to its data complexity and non-ionizing characteristics. For detailed information of SO-PLS method, the readers are referred to the following references [18,21]. A standard linear model of SOPLS algorithm is given as:…”

“…For soil spectroscopy analysis there are several SF methods for this purpose; the most well-known standard analysis method is multi-block PLS regression. Recently, the multi-block SF method, named Sequential Orthogonalized Partial Least square (SOPLS) became highly popular in the food sector to extract relevant information from multi sensor data to predict chemical composition in various food products [18,19]. The advantage of SOPLS is its ability to process multiple source data simultaneously (including both regression and discrimination).…”