Counterfeit or substandard drugs are pharmaceutical formulations in which the active pharmaceutical ingredients (APIs) have been replaced or ingredients do not comply with the drug leaflet. With the outbreak of the COVID-19 pandemic, fraud associated with the preparation of substandard or counterfeit drugs is expected to grow, undermining health systems already weakened by the state of emergency. Analytical chemistry plays a key role in tackling this problem, and in implementing strategies that permit the recognition of uncompliant drugs. In light of this, the present work represents a feasibility study for the development of a NIR-based tool for the quantification of dexamethasone in mixtures of excipients (starch and lactose). Two different regression strategies were tested. The first, based on the coupling of NIR spectra and Partial Least Squares (PLS) provided good results (root mean square error in prediction (RMSEP) of 720 mg/kg), but the most accurate was the second, a strategy exploiting sequential preprocessing through orthogonalization (SPORT), which led (on the external set of mixtures) to an R²_pred of 0.9044, and an RMSEP of 450 mg/kg. Eventually, Variable Importance in Projection (VIP) was applied to interpret the obtained results and determine which spectral regions contribute most to the SPORT model.

In the present work, a fast, relatively cheap, and green analytical strategy to identify and quantify the fraudulent (or voluntary) addition of a drug (alprazolam, the API of Xanax^®) to an alcoholic drink of large consumption, namely gin and tonic, was developed using coupling near-infrared spectroscopy (NIR) and chemometrics. The approach used was both qualitative and quantitative as models were built that would allow for highlighting the presence of alprazolam with high accuracy, and to quantify its concentration with, in many cases, an acceptable error. Classification models built using partial least squares discriminant analysis (PLS-DA) allowed for identifying whether a drink was spiked or not with the drug, with a prediction accuracy in the validation phase often higher than 90%. On the other hand, calibration models established through the use of partial least squares (PLS) regression allowed for quantifying the drug added with errors of the order of 2–5 mg/L.

This study focused on optimizing a cold water extraction method to obtain bioactive compounds from chamomile (Matricaria chamomilla L.), addressing increasing consumer demand for natural products and nutraceuticals. A full-factorial design was employed to evaluate the effects of temperature, time, and chamomile amount on the polyphenolic profile of extracts. The samples were characterized by HPLC-DAD and UV-Vis coupled with chemometrics; the analysis showed that extraction time negatively affected extract quality, as did the interaction between time and temperature. In addition, a significant positive quadratic effect for temperature and a positive coefficient for chamomile amount was found. ASCA was used to assess the UV-Vis profile, offering an alternative untargeted method for understanding the variable effects. The optimal extraction conditions (25 °C, 32 min, and 2.5 g of chamomile) produced samples high in hydroxybenzoic and hydroxycinnamic acids and flavanol derivatives. Using A face-centered design, this study also monitored antioxidant activity via a DPPH scavenging assay, confirming that the optimal conditions yielded samples within the range of maximum antioxidant activity in the studied experimental domain.

This study primarily focuses on the analysis of volatile organic compounds using GC–MS, with ICP-MS employed as a complementary method to quantify trace metal content. Headspace GC–MS was conducted to detect alkylphenol ethoxylates (APEOs), formaldehyde, aromatic amines derived from azo dyes, perfluorinated carboxylic acids, chlorophenols (PCPs), tetrachlorophenols (TPCs), and phthalates in textile samples of different origin and composition. Principal component analysis was used to detect patterns in the volatilome according to the origin and the textile composition. In addition, seven metals (Cr, Ni, Cu, Mo, Cd, Hg, and Pb) were quantified in a subset of samples. The study revealed distinct chemical profiles in textiles based on their origin, with GC–MS identifying key volatile organic compounds and ICP-MS quantifying heavy metals in a subset of samples. Principal component analysis highlighted cotton content as a critical factor in differentiating textile profiles. While most samples adhered to regulatory standards, some exceeded thresholds for metals like copper and nickel, underscoring the need for enhanced quality control in manufacturing processes. By integrating advanced analytical methods, this study provides insights into sustainable and safe textile production, offering valuable benchmarks for regulatory compliance and industry best practices. The outcomes contribute to improving product safety, promoting responsible manufacturing, and supporting regulatory bodies in the enforcement of environmental and safety standards, aligning with the growing demand for sustainability in the textile sector.

Almonds are the seeds of the almond (Prunus Amygdalus) tree and are a nut consumed worldwide. The present study utilized the ATR FT-IR technique followed by a chemometric analysis to develop predictive models for determining the geographical origin of almonds from three regions in Southern Italy (Apulia, Calabria, and Sicily). IR spectra were collected on both the almond shell and the edible kernel to accurately characterize the three different geographical origins. The spectroscopic data obtained were processed using Soft Independent Modeling of Class Analogies (SIMCA) and Partial Least Squares Discriminant Analysis (PLS-DA). Both SIMCA and PLS-DA revealed that the shell spectra are more useful for assessing the geographical origin of samples. In particular, the PLS-DA model applied to these data achieved a 100% correct classification rate (on the external test set of individuals) for all the investigated classes.

In recent years, Brassicaceae have piqued the interest of researchers due to their extremely rich chemical composition, particularly the abundance of antioxidants and anti-inflammatory compounds, as well as because of their antimutagenic and potential anticarcinogenic activity. Vegetables in this family can be found practically everywhere on the planet. In Italy, numerous varieties of Brassicaceae, as well as a diverse pool of local variants, are regularly cultivated. These landraces, which have a variety of peculiar features, have recently sparked increased interest, and the need to safeguard them to preserve genetic biodiversity has become a relevant topic. In the present study, eight distinct Brassicaceae folk varieties were studied using non-destructive tools (Multivariate Image analysis and agro-morphological descriptors). Eventually, the data were handled using explorative analysis (EA) and Soft Independent Modeling by Class Analogy (SIMCA). EA pointed out similarities/dissimilarities among the diverse investigated populations. SIMCA led to high sensitivity (>70%) in prediction (on the external test set) for seven (over eight) investigated classes. Although the investigated plants belong to different landraces, they bear strong similarities. This is mainly linked to the ability of Brassicaceae to hybridize. Despite this, the combination of colorgrams and SIMCA allowed for classifying samples with excellent accuracy.