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Instrument automation, technological advancements and improved computational power made separation science an extremely data-rich approach, requiring the use of statistical and data analysis tools that are able to optimize processes and combine multiple outputs. The use of chemometrics is growing, greatly improving the ability to extract meaningful information. Separation–multidetection generates multidimensional data, whose elaboration should not be left to the discretion of the operator. However, some applications or techniques still suffer from the lack of method optimization through DoE and downstream multivariate analysis, limiting their potential. This review aims at summarizing how chemometrics can assist analytical chemists in terms of data elaboration and method design, focusing on what can be achieved by applying chemometric approaches to separation science. Recent applications of chemometrics in separation analyses, in particular in gas, liquid and size-exclusion chromatography, together with field flow fractionation, will be detailed to visualize the state of the art of separation chemometrics, encompassing volatile, soluble and solid (colloidal) analytes. The samples considered will range from food chemistry and environmental chemistry to bio/pharmaceutical science. [ABSTRACT FROM AUTHOR]

Tomato sauce is a world famous food product. Despite standards regulating the production of tomato derivatives, the market suffers frpm fraud such as product adulteration, origin mislabelling and counterfeiting. Methods suitable to discriminate the geographical origin of food samples and identify counterfeits are required. Chemometric approaches offer valuable information: data on tomato sauce is usually obtained through chromatography (HPLC and GC) coupled to mass spectrometry, which requires chemical pretreatment and the use of organic solvents. In this paper, a faster, cheaper, and greener analytical procedure has been developed for the analysis of volatile organic compounds (VOCs) and the colloidal fraction via multivariate statistical analysis. Tomato sauce VOCs were analysed by GC coupled to flame ionisation (GC-FID) and to ion mobility spectrometry (GC-IMS). Instead of using HPLC, the colloidal fraction was analysed by asymmetric flow field-fractionation (AF4), which was applied to this kind of sample for the first time. The GC and AF4 data showed promising perspectives in food-quality control: the AF4 method yielded comparable or better results than GC-IMS and offered complementary information. The ability to work in saline conditions with easy pretreatment and no chemical waste is a significant advantage compared to environmentally heavy techniques. The method presented here should therefore be taken into consideration when designing chemometric approaches which encompass a large number of samples. [ABSTRACT FROM AUTHOR]

[Display omitted] Advancements in industrial technologies and the application of quality by design (QbD) guidelines are shifting the attention of manufacturers towards innovative production techniques. In the pharmaceutical field, there is a significant focus on the implementation of continuous processes, in which the production stages are carried out continuously, without the need to interrupt the process and store the production intermediates, as in traditional batch production. Such innovative production techniques also require the development of proper analytical methods able to analyze the products in-line, while still being processed. The present study aims to compare a traditional batch manufacturing process with an alternative continuous one. To this end, a real pharmaceutical formulation was used, substituting the active pharmaceutical ingredient (API) with riboflavin, at the concentration of 2 %w/w. Moreover, a direct and non-destructive analytical method based on UV–Vis reflectance spectroscopy was applied for the quantification of riboflavin in the final tablets, and compared with a traditional absorbance analysis. Good results were obtained in the comparison of both the two manufacturing processes and the two analytical methods, with R2 higher than 0.9 for all the calculated calibration models and predicted riboflavin concentrations that never significantly overcame the 15 % limits recommended by the pharmacopeia. The continuous production method demonstrated to be as reliable as the batch one, allowing to save time and money in the production step. Moreover, UV–Vis reflectance was proved to be an interesting alternative to absorption spectroscopy, which, with the proper technology, could be implemented for in-line process control. [ABSTRACT FROM AUTHOR]

Bioindicator plants are species that have the capacity to linearly uptake some elements (metal and metalloids) from the growing substrate, thus reflecting their concentration in the soil. Many factors can influence the uptake of these elements by plants, among which is the simultaneous presence of several metals, a common situation in contaminated or natural soils. A novel approach that can be used to validate the bioindication ability of a species growing on a polymetallic substrate is the design of experiment (DoE) approach. The aim of the present study was to apply the DoE in full factorial mode to model the Cu, Cd, Pb, Zn, and Cr bioindication capacity of Polygonum aviculare, used as the model plant. The results showed that P. aviculare has the ability to bioindicate Cd and Cr with a linear uptake (from 0.35 to 6.66, and 0.1 to 3.4 mg kg−1, respectively) unaffected by the presence of other metals. Conversely, the uptake of Pb, Cu, and Zn is strongly influenced by the presence of all the studied metals, making their concentration in the plant shoot not proportional to that of the soil. In conclusion, these preliminary results confirmed that the DoE can be used to predict the bioindicator abilities of a plant for several elements at the same time and to evaluate the interactions that can be established between variables in the growing medium and in the plant itself. However, more studies including other plant species are needed to confirm the effectiveness of this method. [ABSTRACT FROM AUTHOR]

At present, the geographical origin of extra virgin olive oils can be ensured by documented traceability, although chemical analysis may add information that is useful for possible confirmation. This preliminary study investigated the effectiveness of flash gas chromatography electronic nose and multivariate data analysis to perform rapid screening of commercial extra virgin olive oils characterized by a different geographical origin declared in the label. A comparison with solid phase micro extraction coupled to gas chromatography mass spectrometry was also performed. The new method is suitable to verify the geographic origin of extra virgin olive oils based on principal components analysis and discriminant analysis applied to the volatile profile of the headspace as a fingerprint. The selected variables were suitable in discriminating between “100% Italian” and “non-100% Italian” oils. Partial least squares discriminant analysis also allowed prediction of the degree of membership of unknown samples to the classes examined. [ABSTRACT FROM AUTHOR]

Abstract: A home-made carbon-nanotube paste electrode and multivariate standard-addition method by partial least square regression are employed to pursue improvement of analytical performance in differential pulse stripping voltammetry. Validation is achieved by analyzing reference standard solutions of Pb(II) and evaluating the control parameters: correlation coefficient, prediction coefficient, root-mean square error in calibration and root-mean square error in validation. Validation of multivariate calibration by external-standard method (interpolation) and partial least squares regression is obtained. Analogous validation is obtained also in extrapolation mode by multivariate standard addition method. Application to the analysis of drugs is described, showing the possibility to analyze complex matrices by the rapid, inexpensive and potentially multianalyte and portable voltammetric methodology here presented. [Copyright &y& Elsevier]

This study takes the first step toward in situ analysis of powder drugs which does not require any alteration of the samples. A fast, inexpensive analytical method based on reflectance near-infrared (NIR) spectrometry and multivariate calibration was applied. A diode-array fiber-optic portable spectrometer in the 900-1700 nm range was employed. Samples were laboratory-prepared ternary powders (diacetylmorphine, caffeine, and paracetamol). Partial least squares regression was applied. The choice of the standard samples for calibration and validation was performed through a D-optimal experimental design. The explained variance was higher than 90%, and the relative root mean square errors were <2%. The number of principal components (6) was very low when compared with the number of raw variables (356 absorbance values). Response plots showed slopes and intercepts were very close to optimal values. Correlation coefficients ranged between 0.909 and 0.989. The method here proposed proved to be competitive with Fourier transform NIR spectrometry. [ABSTRACT FROM AUTHOR]

The international seafood trade is based on food safety, quality, sustainability, and traceability. Mussels are bio-accumulative sessile organisms that need regular control to guarantee their safe consumption. However, no well-established and validated methods exist to trace mussel origin, even if several attempts have been made over the years. Recently, an inorganic multi-elemental fingerprint coupled to multivariate statistics has increasingly been applied in food quality control. The mussel shell can be an excellent reservoir of foreign inorganic chemical species, allowing recording long-term environmental changes. The present work investigates the multi-elemental composition of mussel shells, including Al, Cu, Cr, Zn, Mn, Cd, Co, U, Ba, Ni, Pb, Mg, Sr, and Ca, determined by inductively-coupled plasma mass-spectrometry in Mytilus galloprovincialis collected along the Central Adriatic Coast (Marche Region, Italy) at 25 different sampling sites (18 farms and 7 natural banks) located in seven areas. The experimental data, coupled with chemometric approaches (principal components analysis and linear discriminant analysis), were used to create a statistical model able to discriminate samples as a function of their production site. The LDA model is suitable for achieving a correct assignment of >90% of individuals sampled to their respective harvesting locations and for being applied to counteract fraud. [ABSTRACT FROM AUTHOR]

Abstract The production of wine grapes in upstate New York (USA) is limited by diseases that are promoted by the cool and sometimes rainy climate. A breeding program has been introducing disease resistance from related species into the cultivated stock. Previous work has indicated that such resistance may be based on biochemical reactions rather than on a hypersensitive reaction. We therefore undertook metabolic profiling of amino acids and phenolic compounds in berries from collections of susceptible and resistant hybrids over the course of berry development to determine whether any of these compounds could be causal in disease resistance. The most abundant amino acids were GLN, ARG, PRO and THR. The amount of amino acids in ripe berries was from 3 to 4.7-fold higher compared to earlier stages. The concentrations of total phenolics were variable through the season with no consistent trend between susceptible and resistant fruits. Notable changes in phenolic compounds, especially anthocyanins, were recorded, especially during the ripening phase, when phenolics and anthocyanins increased following veraison. The most abundant phenolic compounds were catechin and epi-catechin; the most abundant anthocyanin was delphinidin-3-glucoside, which had a slightly greater concentration in resistant fruit at harvest, followed by malvidin-3-glucoside and petunidin-3-glucoside. The content of both amino acids and phenolic compounds in white-fruited parent cv. Horizon was equal to several-fold lower than the progeny plants, whether susceptible or resistant, depending on the harvest time. While no major differences between susceptible and resistant lines were found, multivariate analyses showed that it is possible to discriminate the susceptibility or resistance of grapes by analyzing their combined concentrations of amino acids, polyphenols and anthocyanins. Therefore, these compounds are influenced by the resistance capacity of grapes and could be used as a chemical fingerprint of this ability. However, it is likely that these are associations with disease resistance rather than their cause as no major consistent differences were noted. Highlights • Amino acids and phenolic compounds in fruits in a hybrid population of grapes differing in disease resistance were analyzed over the developmental season. • The amount of amino acids in ripe berries was from 3 to 4.7-fold higher compared to earlier stages. • Notable changes in phenolic compounds, especially anthocyanins, were recorded, especially during the ripening phase following veraison. • Analysis using LDA and LASSO, unique in plant research, can distinguish populations differing in disease resistance. • The overall difference is probably related to fungal infection rather than a factor in disease resistance. [ABSTRACT FROM AUTHOR]

Honey traceability to food quality is required by consumers and food control institutions. Melissopalynologists traditionally use percentages of nectariferous pollens to discriminate the botanical origin and the entire pollen spectrum (presence/absence, type and quantities and association of some pollen types) to determinate the geographical origin of honeys. To improve melissopalynological routine analysis, principal components analysis (PCA) was used. A remarkable and innovative result was that the most significant pollens for the traditional discrimination of the botanical and geographical origin of honeys were the same as those individuated with the chemometric model. The reliability of assignments of samples to honey classes was estimated through explained variance (85%). This confirms that the chemometric model properly describes the melissopalynological data. With the aim to improve honey discrimination, FT-microRaman spectrography and multivariate analysis were also applied. Well performing PCA models and good agreement with known classes were achieved. Encouraging results were obtained for botanical discrimination. [ABSTRACT FROM AUTHOR]

Abstract The direct quantification of Active Pharmaceutical Ingredients in solid formulations is a challenging open issue. A consolidated analytical technique based on X-ray Powder Diffraction is available, being the definitive test for the identification of polymorphs and crystal phases. However, its application for quantitative analysis is hindered by matrix effects: refinement methods (e.g. Rietveld method) require a complete knowledge of samples' composition, while univariate calibration methods require the matrix effect to be studied and severely suffer from the co-presence of crystalline and amorphous phases in the sample. Multivariate analysis is the only way to bypass problems affecting refinements procedures and univariate calibration. In particular, the multivariate standard addition method (SAM) is promising; however, it is straightforward only when the analytical blank (matrix devoid of analyte) is available: in that case SAM is applied by simply extrapolating the SAM model to the matrix experimental signal. In this work, the quantitative analysis of polymorphic forms of Active Pharmaceutical Ingredients based on X-ray Powder Diffraction is performed for the first time by a method based on multivariate standard addition method combined with net analyte signal procedure; it allows for reliable quantification of polymorphs of active principles in solid formulations, which are rapidly analyzed without any sample pre-treatment. Two test cases are presented: quantification of two polymorphs of piracetam in binary mixtures (forms II and III), and quantification of paracetamol (form I) in Tachifludec®. Graphical abstract Unlabelled Image [ABSTRACT FROM AUTHOR]

In 2020 North Italy suffered the SARS-CoV-2-related pandemic with a high number of deaths and hospitalization. The effect of atmospheric parameters on the amount of hospital admissions (temperature, solar radiation, particulate matter, relative humidity and wind speed) is studied through about 8 months (May–December). Two periods are considered depending on different conditions: a) low incidence of COVID-19 and very few regulations concerning personal mobility and protection ("free/summer period"); b) increasing incidence of disease, social restrictions and use of personal protections ("confined/autumn period"). The "hospitalized people in medical area wards/100000 residents" was used as a reliable measure of COVID-19 spreading and load on the sanitary system. We developed a chemometric approach (multiple linear regression analysis) using the daily incidence of hospitalizations as a function of the single independent variables and of their products (interactions). Eight administrative domains were considered (altogether 26 million inhabitants) to account for relatively homogeneous territorial and social conditions. The obtained models very significantly match the daily variation of hospitalizations, during the two periods. Under the confined/autumn period, the effect of non-pharmacologic measures (social distances, personal protection, etc.) possibly attenuates the virus diffusion despite environmental factors. On the contrary, in the free/summer conditions the effects of atmospheric parameters are very significant through all the areas. Particulate matter matches the growth of hospitalizations in areas with low chronic particulate pollution. Fewer hospitalizations strongly correspond to higher temperature and solar radiation. Relative humidity plays the same role, but with a lesser extent. The interaction between solar radiation and high temperature is also highly significant and represents surprising evidence. The solar radiation alone and combined with high temperature exert an anti-SARS-CoV-2 effect, via both the direct inactivation of virions and the stimulation of vitamin D synthesis, improving immune system function. • Temperature, sun radiation, particulate matter, % humidity, wind vs hospitalization. • Multiple linear regression analysis has been performed to create multivariate models. • Two time periods free of restrictions and with restrictions has been considered. • Temperature, solar radiation, and their interaction match with fewer hospitalization. • Particulate matter matches higher hospitalization in areas with low chronic pollution. [ABSTRACT FROM AUTHOR]