Browsing by Subject "Chemometrics"

Now showing 1 - 3 of 3
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    Chemometric approach for profiling of metabolites of potential antioxidant activity in Apiaceae species based on LC-PDA-ESI-MS/MS and FT-NIR
    (2023) Atta, Noha H.; Handoussa, Heba; Klaiber, Iris; Hitzmann, Bernd; Hanafi, Rasha S.
    Chemometrics is a tool for data mining and unlocking the door for solving big data queries. Apiaceae is a family species which is commonly cultivated worldwide. Although members of this species are widely used as antioxidant, antibacterial, antifungal, and anti-inflammatory agents, their metabolites profiling remains ambiguous. Based on WHO support, chemometrics has been used in evaluating the quality and authenticity of the herbal products. The objective of this study is to profile and characterize phenolic metabolites in nine species from Egyptian cultivars and three different species of German cultivars from the Apiaceae family using multivariate analysis after LC-PDA-ESI-MS/MS and near infrared spectroscopy data are generated. Principal component analysis was successfully applied to distinguish between the nine Egyptian cultivars and the three German cultivars, and hierarchical cluster analysis also confirmed this distinctive clustering. Partial least square regression (PLS-R) models showed a relationship between phytochemicals and antioxidant activities. The metabolites responsible for the clustering pattern and variables important for projection (VIP) were identified, being twelve amongst nine Egyptian cultivar samples and thirteen amongst the Egyptian cultivar and the German cultivar comparison. The identified VIPs were also correlated with the antioxidant activity using PLS-R. In conclusion, the study showed novelty in the application of hyphenated analytical techniques and chemometrics that assist in quality control of herbal medicine.
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    Fluorescence spectroscopy and chemometrics : an innovative approach for characterization of wheat flour and dough preparation
    (2016) Ahmad, Muhammad Haseeb; Hitzmann, Bernd
    Implementation of process analytical technologies (PAT) in food applications has attained a remarkable motivation due to higher quality and safety standards in this field. PAT applications also include rapid and non-invasive approaches which can be obtained from spectroscopic techniques. Fluorescence spectroscopy together with chemometrics is considered to be an outstanding analytical tool for fast and non-invasive technique for food analysis which can be used in various food applications on industrial scale. It is known for its sensitivity and specificity which can analyze the different foods and its ingredients while chemometrics helps to extract the useful information from the spectral data. The different chemometrics tools used for quantitative and qualitative analysis of spectral data, has increased the importance of this spectroscopic technique in generating the new ideas and hypothesis to develop new analytical methods which lead towards betterment in industrial operations for process and quality monitoring. In this doctoral project, fluorescence spectroscopic together with chemometric has been utilized to develop some new methods for determination of different parameters of wheat which provides the central idea of the thesis. First manuscript presents the potential of fluorescence spectroscopy to predict the analytical, rheological and baking parameters of different wheat flours by just taking the spectral signature without any sample preparation. Twelve different wheat flours milled from wheat cultivars were used to analyze the analytical, rheological and baking parameters using the conventional methods. These measured parameters were predicted from the spectral data taken for different wheat flours using genetic algorithm coupled with partial least square regression. The model obtained for protein, wet gluten and sedimentation value showing high R2 = 0.90, 0.92 and 0.81 respectively. Similarly, the rheological parameters like dough development time and water absorption were also predicted with low root mean square error of cross validation (RMSECV) and high R2 = 0.95 and 0.77 respectively while pasting temperature showed R2 = 0.78. Furthermore, moisture and volume of bread were predicted with high accuracy showing R2 = 0.86 and 0.95 respectively in the baking parameters. Other rheological and baking parameters like dough stability, softening, farinograph quality number, baking loss, crumb hardness and springiness were not predicted well due to poor correlation and high error. In the second paper, characterization of complex farinographic kneading process is performed by using the fluorescence spectroscopy in combination with chemometric tools. The aim of this investigation is to determine the impact of hydration of flour onto the spectral signals, classification of farinographic curve and separation of wheat flours based on their bread making performance. Secondly the middle curve of farinograph was predicted out of the fluorescence spectra using partial least square regression (PLSR) which can help to predict optimal dough development time. The spectra of the flour showed high intensities in protein, NADH and riboflavin regions which reduce to 36 %, 58 % and 61 % respectively after the hydration process depicting its influence due to structural changes in protein and oxidation of NADH. The farinographic curve was divided into four phases and principal component analysis (PCA) has been used to extract the qualitative information regarding the farinographic curve from the fluorescence spectra to categorize all farinographic phases into hydration, dough development, and stability and softening. Similarly, different pre-processing tools like standard normal variate and generalized least square weighting generate good separation of various wheat flours during the farinographic kneading process into different quality groups (E, A, B and C) on the basis of their bread baking performance from the spectral data using PCA. Additionally, PLSR was applied to predict the middle curve of farinograph out of spectral data showing a descent coefficient of determination R2 = 0.75 with RMSECV of 14 Brabender units. However, more research can lead towards the development of a sensor for determination of optimal dough development time. In another study, the nutritional parameters of 26 different types of wheat flour obtained from different vendors from the supermarket were predicted using fluorescence coupled with linear and non-linear chemometric tools. PCA applied on the spectral data for different types of the wheat flours showing a clear separation. On the other hand, PLSR was used to quantify the nutritional parameters of different types of wheat flours showing a good prediction for fat, moisture and carbohydrates using cross-validation, with a R2 of 0.88, 0.86 and 0.89, respectively whereas the protein, sucrose and salt contents presented a little correlation in PLSR. Therefore, locally weighted regression, a non-linear chemometric tool improves the prediction ability of all of the nutritional parameters by decreasing the error with an increasing R2. The energetic value, protein, fat, carbohydrate, moisture, sucrose, salt and saturated fatty acid contents showed R2 of 0.96, 0.93, 0.99, 0.99, 0.98, 0.88, 0.95, and 0.99 respectively, for different wheat flours. The aforementioned results clearly demonstrate the potential of the fluorescence spectroscopy in determination of analytical, rheological, baking and nutritional parameters of the wheat flours. They present that it can be used to characterize and categorize the farinographic kneading process, which is important in the bread-baking industry. More research in this direction can result in developing a sensor for predicting the quality parameters and processing operations in the cereal based industries rapidly and non-invasively which are important for regulatory and screening of the wheat on quality characteristics for marketing and end product evaluations.
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    Generic chemometric models for metabolite concentration prediction based on Raman spectra
    (2022) Yousefi-Darani, Abdolrahim; Paquet-Durand, Olivier; von Wrochem, Almut; Classen, Jens; Tränkle, Jens; Mertens, Mario; Snelders, Jeroen; Chotteau, Veronique; Mäkinen, Meeri; Handl, Alina; Kadisch, Marvin; Lang, Dietmar; Dumas, Patrick; Hitzmann, Bernd
    Chemometric models for on-line process monitoring have become well established in pharmaceutical bioprocesses. The main drawback is the required calibration effort and the inflexibility regarding system or process changes. So, a recalibration is necessary whenever the process or the setup changes even slightly. With a large and diverse Raman dataset, however, it was possible to generate generic partial least squares regression models to reliably predict the concentrations of important metabolic compounds, such as glucose-, lactate-, and glutamine-indifferent CHO cell cultivations. The data for calibration were collected from various cell cultures from different sites in different companies using different Raman spectrophotometers. In testing, the developed “generic” models were capable of predicting the concentrations of said compounds from a dilution series in FMX-8 mod medium, as well as from an independent CHO cell culture. These spectra were taken with a completely different setup and with different Raman spectrometers, demonstrating the model flexibility. The prediction errors for the tests were mostly in an acceptable range (<10% relative error). This demonstrates that, under the right circumstances and by choosing the calibration data carefully, it is possible to create generic and reliable chemometric models that are transferrable from one process to another without recalibration.