Quality assessment of the saffron samples using second-order spectrophotometric data assisted by three-way chemometric methods via quantitative analysis of synthetic colorants in adulterated saffron

Interference-free quantitation of aromatic amino acids in two complex systems by three-way calibration with ultraviolet-visible spectrophotometer: Exploration of trilinear decomposition of spectrum-pH data

Aromatic amino acids play an extremely important role in life activities and participate in many biological processes. Their concentration levels are associated with a variety of diseases, such as phenylketonuria and colorectal cancer. Therefore, the quantification of aromatic amino acids is an important task. In the present work, a novel and rapid three-way analytical method was proposed to detect the levels of aromatic amino acids in prostate cancer cells (PC3 cells) and Dulbecco's modified minimal essential medium (DMEM cell culture), by using the affordable ultraviolet-visible spectrophotometer. First, spectrum-pH second-order data were designed per sample; Second, properties of the resulted spectrum-pH-sample three-way data were investigated by utilizing the parallel factor analysis (PARAFAC), alternating trilinear decomposition (ATLD), and constrained alternating trilinear decomposition (CATLD) algorithms, and a flexible scanning approach for determining the constraint parameters of CATLD was proposed; Third, a three-way calibration method based on the CATLD algorithm with the proposed scanning approach was developed for interference-free quantification of aromatic amino acids in these systems. The average relative predictive errors of validation (ARPEV) for phenylalanine, tyrosine, and tryptophan were 1.4%, 3.0%, and 0.7% in prostate cancer cells, and ARPEV for phenylalanine, tyrosine, and tryptophan were 4.1%, 1.2%, and 0.7% in DMEM cell culture. The predicted contents of tyrosine and tryptophan in DMEM cell culture were 64.2 ± 2.9 μg mL^-1, 5.6 ± 0.3 μg mL^-1, there are no significant differences in the concentrations between the developed analytical method and high performance liquid chromatography method. The proposed spectrum-pH-sample three-way calibration method based on CATLD algorithm can provide an interesting analytical strategy with high selectivity and accuracy for ultraviolet-visible spectrophotometer.

Detection of Saffron’s Main Bioactive Compounds and Their Relationship with Commercial Quality

This review aims to evaluate the state of saffron’s main bioactive compounds and their relationship with its commercial quality. Saffron is the commercial name for the dried red stigmas of the Crocus sativus L. flower. It owes its sensory and functional properties mainly to the presence of its carotenoid derivatives, synthesized throughout flowering and also during the whole production process. These compounds include crocin, crocetin, picrocrocin, and safranal, which are bioactive metabolites. Saffron’s commercial value is determined according to the ISO/TS3632 standard that determines their main apocatotenoids. Other techniques such as chromatography (gas and liquid) are used to detect the apocarotenoids. This, together with the determination of spectral fingerprinting or chemo typing are essential for saffron identification. The determination of the specific chemical markers coupled with chemometric methods favors the discrimination of adulterated samples, possible plants, or adulterating compounds and even the concentrations at which these are obtained. Chemical characterization and concentration of various compounds could be affected by saffron’s geographical origin and harvest/postharvest characteristics. The large number of chemical compounds found in the by-products (flower parts) of saffron (catechin, quercetin, delphinidin, etc.) make it an interesting aromatic spice as a colorant, antioxidant, and source of phytochemicals, which can also bring additional economic value to the most expensive aromatic species in the world.

Method for Data Quality Assessment of Synthetic Industrial Data

Sometimes it is difficult, or even impossible, to acquire real data from sensors and machines that must be used in research. Such examples are the modern industrial platforms that frequently are reticent to share data. In such situations, the only option is to work with synthetic data obtained by simulation. Regarding simulated data, a limitation could consist in the fact that the data are not appropriate for research, based on poor quality or limited quantity. In such cases, the design of algorithms that are tested on that data does not give credible results. For avoiding such situations, we consider that mathematically grounded data-quality assessments should be designed according to the specific type of problem that must be solved. In this paper, we approach a multivariate type of prediction whose results finally can be used for binary classification. We propose the use of a mathematically grounded data-quality assessment, which includes, among other things, the analysis of predictive power of independent variables used for prediction. We present the assumptions that should be passed by the synthetic data. Different threshold values are established by a human assessor. In the case of research data, if all the assumptions pass, then we can consider that the data are appropriate for research and can be applied by even using other methods for solving the same type of problem. The applied method finally delivers a classification table on which can be applied any indicators of performed classification quality, such as sensitivity, specificity, accuracy, F1 score, area under curve (AUC), receiver operating characteristics (ROC), true skill statistics (TSS) and Kappa coefficient. These indicators’ values offer the possibility of comparison of the results obtained by applying the considered method with results of any other method applied for solving the same type of problem. For evaluation and validation purposes, we performed an experimental case study on a novel synthetic dataset provided by the well-known UCI data repository.