Sylwester Mazurek

Pierwsza część pracy opisuje badania binarnych mieszanin wody z dwoma grupami rozpuszczalników organicznych: ketonami i alkoholami alifatycznymi. Przedmiotem badań były związki o ograniczonej mieszalności z wodą i zróżnicowanej strukturze, co pozwoliło na otrzymanie bardziej ogólnych wniosków na temat stanu wody w tych rozpuszczalnikach oraz korelacji pomiędzy rozpuszczalnością a strukturą cząsteczek rozpuszczalnika. Podstawowym narzędziem badawczym była spektroskopia w zakresie bliskiej-podczerwieni (NIR) a zawartość wody w nasyconych roztworach wyznaczano za pomocą miareczkowania metodą Karla-Fischera. Interpretacja wyników eksperymentalnych była wspomagana za pomocą teoretycznych obliczeń (DFT) struktury i energii wybranych kompleksów wody z badanymi rozpuszczalnikami. W części drugiej przypomniano podstawy spektroskopowej analizy ilościowej. Przedstawiono wyniki oznaczeń zawartości kwasu foliowego, hydrochlorotiazydu, loratadyny, furosemidu, ketoprofenu i metforminy w tabletkach, w oparciu o modele PLS opracowane na bazie widm ATR, DRIFTS i Ramana. Uzyskany odzysk substancji aktywnej mieści się w przedziale 0.991-1.015 i nie zależy od zawartości API oraz użytej metody. Dla preparatów o małym stężeniu API (0.06-0.12um) otrzymano błędy RSEPwal w przedziale 2-5%, dla tych o większym (0.4-0.9um) wznoszą one 0.3-2%. W podobny sposób można oznaczyć zawartość substancji aktywnych i składników odżywczych w produktach pochodzenia naturalnego, co pokazano na przykładzie analizy zawartości białka w pyłku pszczelim. W ostatniej sekcji opisano zastosowanie technik spektroskopii oscylacyjnej w połączeniu z analizą wielowymiarową w diagnostyce spektroskopowej. Widma płynów ustrojowych stanowią cenne źródło informacji o stanie zdrowia i mogą być wykorzystane do opracowania protokołów analitycznych wspomagających procedury diagnostyczne, szczególnie w przypadku braku wiarygodnych markerów umożliwiających jednoznaczną identyfikację choroby. Do opracowania modeli dyskryminacyjnych wykorzystano metody uczenia maszynowego i uczenia głębokiego. Przedstawiono wyniki analiz na bazie widm oscylacyjnych surowicy krwi uzyskanych od pacjentów z zaawansowaną endometriozą i cierpiących na choroby reumatoidalne, podkreślając, w szczególności, potencjał modelowania hybrydowego.

Rheumatoid arthritis (RA) and psoriatic arthritis (PsA) are chronic autoimmune diseases. They share similar symptoms. The lack of specific markers can lead to misdiagnosis. Using spectroscopic information on the chemical composition of body fluids can effectively differentiate these diseases. The discriminant analysis results are presented based on Raman and near-infrared (NIR) spectra of freeze-dried blood sera. The performance of partial least squares discriminant analysis (PLS-DA) and counter-propagation artificial neural network (CP-ANN) techniques in differentiation between RA (n = 30) and PsA (n = 24) patients and healthy controls (HC, n = 15) were compared. Samples were divided into calibration and validation sets using a Kennard–Stone algorithm; approximately 1/3 of the samples were selected for external validation. The PLS-DA and CP-ANN models built based on spectral features selected using the interval partial least squares (iPLS) algorithm resulted in an overall accuracy (OA) for test samples prediction in the 81.3–93.8% range. Hybrid models elaborated using a combination of selected biochemical parameters of blood serum and spectral variables were characterized by OA values from 87.5 to 93.8%. The obtained results confirm that vibrational spectroscopy and chemometric modeling enable discrimination of these two difficult-to-diagnose diseases on the basis of spectral data of the dried blood serum.

Diagnosing female infertility is a complex and time-consuming task due to the large number of factors affecting the patient’s fertility, which results in the need to perform many tests to determine the cause in each case accurately. In recent years, the use of spectroscopic methods has been explored for their potential to identify spectral markers of female infertility through analysis of follicular fluid (FF). This article aims to serve as a review and presentation of the research performed in the field of female infertility diagnostics using NMR and vibrational spectroscopy in the analysis of FF samples.

Vibrational spectroscopy, including Raman and near-infrared techniques, enables the non-destructive evaluation of starch gelatinization, head rice yield, and aroma-active volatile compounds in parboiled rice subjected to varying soaking and drying conditions. Raman and NIR spectra were collected for rice samples processed under different conditions and integrated with reference analyses to develop and validate partial least squares regression and artificial neural network models. The optimized PLSR model demonstrated strong predictive performance, with R² values of 0.9406 and 0.9365 for SG and HRY, respectively, and residual predictive deviations of 3.98 and 3.75 using Raman effective wavelengths. ANN models reached R² values of 0.97 for both SG and HRY, with RPDs exceeding 4.2 using NIR effective wavelengths. In the aroma compound analysis, p-Cymene exhibited the highest predictive accuracy, with R² values of 0.9916 for calibration, and 0.9814 for cross-validation. Other volatiles, such as 1-Octen-3-ol, nonanal, benzaldehyde, and limonene, demonstrated high predictive reliability (R² ≥ 0.93; RPD > 3.0). Conversely, farnesene, menthol, and menthone showed poor predictability (R² < 0.15; RPD < 0.4). Principal component analysis revealed that the first principal component explained 90% of the total variance in the Raman dataset and 71% in the NIR dataset. Hotelling’s T² analysis identifies influential outliers and enhances model robustness. Optimal processing conditions for achieving maximum HRY and SG values were determined at 65 °C soaking for 180 min, followed by drying at 70 °C. This study underscores the potential of integrating vibrational spectroscopy with machine learning techniques and targeted wavelength selection for the high-throughput, accurate, and scalable quality evaluation of parboiled rice.

BACKGROUND: Determination of composition and physicochemical parameters of natural products requires dedicated, often laborious and expensive, analytical protocols. Different spectroscopic techniques, in conjunction with chemometrics, seem to have a considerable potential in direct analysis of raw plant material and foods, without any chemical treatment.RESULTS: Fluorescence spectroscopy and three vibrational spectroscopy techniques were applied to determine total polyphenol content, antioxidant activity and macronutrient levels in red‐ and purple‐fleshed potato varieties. Excitation–emission matrix fluorescence, Fourier transform Raman, attenuated total reflection Fourier transform infrared and near‐infrared spectra were recorded for the freeze‐dried samples. Combining spectral data and the results of reference analyses, partial least squares regression models were constructed for each parameter studied. For polyphenols and antioxidant activity, quantification errors found for validation samples amounted to 3.74–5.04% and 4.75–6.35%, respectively, whereas macronutrient analysis gave errors in the 3.45–4.55%, 3.09–5.30% and 5.10–8.58% ranges for starch, protein and sugar determinations, respectively.CONCLUSION: The obtained results demonstrate that different spectroscopic techniques in combination with multivariate modeling allow simultaneous determination of various parameters of plant samples based on a single sample spectrum. They can effectively replace commonly used protocols of food product analysis requiring sample dissolving and extraction of the compounds of interest. © 2023 Society of Chemical Industry.

The prevalence of lifestyle diseases and trends related to healthy eating contribute to the constant search for chemical compounds with specific biological activity. Studies are conducted on plants and substances of natural origin that have been used in medicine for millennia. Techniques of vibrational spectroscopy are an underrated group of methods enabling direct analysis of plant raw material and food in their native forms. The presented examples of Arabidopsis tissues, various species and hybrids of poplar and Cistus herb classification, as well as quantitative analyses of active compounds in plant material and pharmaceutical products and determination of physicochemical parameters of common food (i.e. milk, yoghurts, pasta and flour), demonstrate the possibility of using vibrational spectroscopy for comprehensive analysis of samples of natural origin. Typical measurement techniques and chemometric methods are briefly described in this paper. The scheme of quantitative analysis based on vibrational spectra is shown and the impact of selected experimental parameters on the accuracy of the obtained results is discussed. The imaging techniques used to analyse the changes in plant tissue structures caused by genetic mutations were also presented

Cistus is a plant that has been used in natural medicine for hundreds of years; it works primarily as an antioxidant and cleansing agent. Cistus × incanus leaves or herb can be an attractive source of polyphenols and flavonoids. The official protocols of active compound analysis relies on the extraction of compounds of interest from plant matter, which makes their determination long and costly. An analysis of plant material in its native state can be performed using vibrational spectroscopy. This paper presents a comparison of Raman spectroscopy, attenuated total reflection in mid-infrared and diffuse reflectance technique in the near-infrared region for the simultaneous quantification of total polyphenols (TPC) and flavonoids (TF) content, as well as the determination of FRAP antioxidant activity of C. incanus material. Utilizing vibrational spectra and using partial least squares algorithm, TPC and TF were quantified with the RSEP_VAL errors in the 2.7–5.4% range, while FRAP antioxidant activity for validation sets was determined with relative errors ranged from 5.2 to 9.3%. For the analyzed parameters, the lowest errors of predictions were computed for models constructed using Raman data. The developed models allow for fast and precise quantification of the studied active compounds in C. incanus material without any chemical sample treatment.