Chemical profiling of floral and chestnut honey using high-performance liquid chromatography-ultraviolet detection

Comparing baseline correction algorithms in discriminating brownish soils from five proximity locations based on UPLC and PLS-DA methods

Soil is commonly collected from an outdoor crime scene, and thus it is helpful in linking a suspect and a victim to a crime scene. The chemical profiles of soils can be acquired via chemical instruments such as Ultra-Performance Liquid Chromatography (UPLC). However, the UPLC chromatogram often interferes with an unstable baseline. In this paper, we compared the performance of five baseline correction (BC) algorithms, i.e. asymmetric least squares (AsLS), fill peak, iterative restricted least squares, median window (MW), and modified polynomial fitting, in discriminating 30 chromatograms of brownish soils by five locations of origin, i.e. PP, HK, KU, BL, and KB. The performances of the preprocessed sub-datasets were first visually inspected through the mean chromatograms and then further explored via scores plots of principal component analysis (PCA). Eventually, the predictive performances of the partial least squares-discriminant analysis (PLS-DA) models estimated from 1 000 pairs of training and testing samples (i.e. prepared via iterative random resampling split at 75:25) were studied to identify the best BC method. Mean raw chromatograms of the 10 soil samples were different from each other, with evident fluctuated baselines. AsLS and MW corrected chromatograms demonstrated the most significant improvement compared with the raw counterpart. Meanwhile, the scores plot of PCA revealed that most of the sub-datasets produced three separate clusters. Then, the sub-datasets were modelled via the PLS-DA technique. MW emerged as the excellent BC method based on the mean prediction accuracy estimated using 1 000 pairs of training and testing samples. In conclusion, MW outperformed the other BC methods in correcting the UPLC data of soil.

Key points

UPLC data of soil interfere with baseline drifts.BC can improve the quality of the pixel-level UPLC data.MW emerges as the most desired algorithm in improving the quality of UPLC data of soil.

Honey Phenolic Compound Profiling and Authenticity Assessment Using HRMS Targeted and Untargeted Metabolomics

Honey consumption is attributed to potentially advantageous effects on human health due to its antioxidant capacity as well as anti-inflammatory and antimicrobial activity, which are mainly related to phenolic compound content. Phenolic compounds are secondary metabolites of plants, and their content in honey is primarily affected by the botanical and geographical origin. In this study, a high-resolution mass spectrometry (HRMS) method was applied to determine the phenolic profile of various honey matrices and investigate authenticity markers. A fruitful sample set was collected, including honey from 10 different botanical sources (n = 51) originating from Greece and Poland. Generic liquid-liquid extraction using ethyl acetate as the extractant was used to apply targeted and non-targeted workflows simultaneously. The method was fully validated according to the Eurachem guidelines, and it demonstrated high accuracy, precision, and sensitivity resulting in the detection of 11 target analytes in the samples. Suspect screening identified 16 bioactive compounds in at least one sample, with abscisic acid isomers being the most abundant in arbutus honey. Importantly, 10 markers related to honey geographical origin were revealed through non-targeted screening and the application of advanced chemometric tools. In conclusion, authenticity markers and discrimination patterns were emerged using targeted and non-targeted workflows, indicating the impact of this study on food authenticity and metabolomic fields.

Alternative data mining/machine learning methods for the analytical evaluation of food quality and authenticity - A review

In recent years, the variety and volume of data acquired by modern analytical instruments in order to conduct a better authentication of food has dramatically increased. Several pattern recognition tools have been developed to deal with the large volume and complexity of available trial data. The most widely used methods are principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), soft independent modelling by class analogy (SIMCA), k-nearest neighbours (kNN), parallel factor analysis (PARAFAC), and multivariate curve resolution-alternating least squares (MCR-ALS). Nevertheless, there are alternative data treatment methods, such as support vector machine (SVM), classification and regression tree (CART) and random forest (RF), that show a great potential and more advantages compared to conventional ones. In this paper, we explain the background of these methods and review and discuss the reported studies in which these three methods have been applied in the area of food quality and authenticity. In addition, we clarify the technical terminology used in this particular area of research.