In the present work, the applicability of attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy, coupled with chemometric tools in recognizing essential oils (EOs) for routine control, was evaluated. EOs belonging to Mentha, Cymbopogon, and Lavandula families and to S. rosmarinus and T. vulgaris species were analyzed, and the performance of several untargeted approaches, based on the synergistic combination of ATR-FTIR and Partial Least Squares Discriminant Analysis (PLS-DA), was tested to classify the species and chemotypes. Different spectra pre-processing methods were employed, and the robustness of the built models was tested by means of a Receiver Operating Characteristic (ROC) curve and random permutations test. The application of these approaches revealed fruitful results in terms of sensitivity and specificity, highlighting the potentiality of ATR-FTIR and chemometrics techniques to be used as a sensitive, cost-effective, and rapid tool to differentiate EO samples according to their species and chemotype.

Raman spectroscopy, and handheld spectrometers in particular, are gaining increasing attention in food quality control as a fast, portable, non-destructive technique. Furthermore, this technology also allows for measuring the intact sample through the packaging and, with respect to near infrared spectroscopy, it is not affected by the water content of the samples. In this work, we evaluate the potential of the methodology to model, by multivariate data analysis, the authenticity of Parmigiano Reggiano cheese, which is one of the most well-known and appreciated hard cheeses worldwide, with protected denomination of origin (PDO). On the other hand, it is also highly subject to counterfeiting. In particular, it is critical to assess the authenticity of grated cheese, to which, under strictly specified conditions, the PDO is extended. To this aim, it would be highly valuable to develop an authenticity model based on a fast, non-destructive technique. In this work, we present preliminary results obtained by a handheld Raman spectrometer and class-modeling (Soft Independent Modeling of Class Analogy, SIMCA), which are extremely promising, showing sensitivity and specificity of 100% for the test set. Moreover, another salient issue, namely the percentage of rind in grated cheese, was addressed by developing a multivariate calibration model based on Raman spectra. It was possible to obtain a prediction error around 5%, with 18% being the maximum content allowed by the production protocol.

The metal content in some samples of horse chestnut seeds (Aesculus hippocastanum) was monitored over time (years 2016–2019) considering the two most common and representative Mediterranean varieties: the pure species (AHP, which gives white flowers) and a hybrid one (AHH, which gives pink flowers). The selected elemental composition of the samples was determined by applying the Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES) technique. Several samples obtained from different preliminary treatments of the peeled seeds were examined, such as: (i) floury samples (wild-type) mineralized with the wet method; (ii) the ashes of both AHP and AHH varieties; (iii) the fraction of total inorganic soluble salts (TISS). Furthermore, the hydroalcoholic crude extracts (as a tincture) were obtained according to the official Pharmacopoeia methods, and the relevant results were compared with those of a commercial sample, an herbal product-food supplement of similar characteristics. The main characteristics of this research work underline that the two botanical varieties give different distinctive characters, due to the Fe content (80.05 vs. 1.42 mg/100 g d.s., for AHP and AHH wild-type flour samples, respectively), along with K, Ca, Mn, Ni and Cu, which are more abundant in the AHP samples. Furthermore, the Principal Component Analysis (PCA) was applied to the experimental dataset in order to classify and discriminate the samples, in relation to their similar botanical origin, but different for the color of the bloom. These results can be useful for the traceability of raw materials potentially intended for the production of auxiliary systems of pharmacological interest.

The aim of this work is to assess the potentialities of the synergistic combination of an ultra-fast chromatography-based electronic nose as a fingerprinting technique and multivariate data analysis in the context of food quality control and to investigate the influence of some factors, i.e., basil variety, cut, and year of crop, in the final aroma of the samples. A low = level data fusion approach coupled with Principal Component Analysis (PCA) and ANOVA—Simultaneous Component Analysis (ASCA) was used in order to analyze the chromatographic signals acquired with two different columns (MXT-5 and MXT-1701). While the PCA analysis results highlighted the peculiarity of some basil varieties, differing either by a higher concentration of some of the detected chemical compounds or by the presence of different compounds, the ASCA analysis pointed out that variety and year are the most relevant effects, and also confirmed the results of previous investigations.