Analysis of Moisture Content in Beetroot using Fourier Transform Infrared Spectroscopy and by Principal Component Analysis

Estimation of Proximate Composition in Rice Using ATR-FTIR Spectroscopy and Chemometrics

This study presents an innovative approach for estimating the proximate composition of diverse rice varieties using attenuated total reflectance fourier transform infrared (ATR-FTIR) spectroscopy and chemometric techniques. Principal component analysis (PCA) reveals distinct separations among the seven rice varieties based on their FTIR spectra. Robust partial least squares (PLS) regression models, developed with high calibration (R 2) values from 0.778 for protein up to 0.941 for moisture, demonstrate high accuracy in predicting proximate composition. The root mean squared error (RMSE) in percentage values, indicative of prediction accuracy, were low across all proximate components. To ensure the response variable of regression, proximate composition measurements were taken five times, while FTIR spectra were scanned tens of times, employing random numbers around the average with the same standard deviation as the measurement. Notably, the study emphasizes the pivotal role of the amide-III band in protein determination, alongside specific wavenumber regions associated with molecular changes in proximate components. This research underscores the potential of ATR-FTIR spectroscopy and chemometrics for rapid and accurate proximate assessment in food science and agriculture.

Mechanical Enhancement and Water Treatment Efficiency of Nanocomposite PES Membranes: A Study on Akçay Dam Water Filtration Application

Polymeric membranes are widely used in water treatment because of their ease of fabrication and low cost. The flux and purification performance of membranes can be significantly improved by incorporating appropriate amounts of nanomaterials into the polymeric membrane matrices. In this study, neat poly(ether sulfone) (PES), PES/nano copper oxide (CuO), and PES/nano zinc oxide (ZnO) membranes are fabricated via phase inversion. The pure water flux of the neat PES membrane, which is 355.14 L/m2·h, is increased significantly with the addition of nano-CuO and nano-ZnO, and the pure water fluxes of the nanocomposite membranes vary in the range of 392.65-429.74 L/m2·h. Moreover, nano CuO and nano ZnO-doped PES nanocomposite membranes exhibit higher conductivity, color, total organic carbon, boron, iron, selenium, barium, and total chromium removal efficiencies than neat PES membranes. The membrane surfaces examined by Scanning Electron Microscopy (SEM) after water filtration revealed that those containing 0.5% wt. nano CuO and nano ZnO are more resistant to fouling than the membrane surfaces containing 1% wt. nano CuO and nano ZnO. Based on the results of this study, 0.5% wt. nano ZnO-doped PES membrane is found to be the most suitable membrane for use in water treatment due to its high pure water flux (427.14 L/m2·h), high pollutant removal efficiency, and high fouling resistance. When the mechanical properties of the membranes are examined, the addition of CuO and ZnO nanoparticles increases the membrane stiffness and modulus of elasticity. The addition of 0.5% and 1% for CuO leads to an increase in the modulus of elasticity by 57.95% and 324.43%, respectively, while the addition of 0.5% and 1% for ZnO leads to an increase in the modulus of elasticity by 480.68% and 1802.43%, respectively. At the same time, the tensile strength of the membranes also increases with the addition of nanomaterials.

Multivariate Statistical Analysis for the Classification of Sausages Based on Physicochemical Attributes, Using Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) and Inductively Coupled Plasma-Mass Spectrometry (ICP-MS)

Sausage is a convenient food that is widely consumed in the world and in Vietnam. Due to the rapid development of this product, the authenticity of many famous brands has faded by the rise of adulteration. Therefore, in this study, principal component analysis (PCA) was combined with chemical analysis to identify 6 sausage brands. Sausage samples were dried and then ground to a fine powder for both instrumental analyses of attenuated total reflectance-Fourier transform infrared (ATR-FTIR) and inductively coupled plasma-mass spectrometry (ICP-MS). Dried measurements of ATR-FTIR was performed directly on the ZnSe crystal, while elemental data were obtained through microwave digestion before the ICP-MS analysis. Principal component analysis (PCA) within the framework software of XLSTAT and STATISTICA 12 was performed on the spectroscopy and elemental dataset of sausage samples. PCA visualized the distinction of 6 sausage brands on both datasets of ATR-FTIR and ICP-MS. The classification on the spectroscopy profile showed that although more than 90% variation of the dataset was explained on the first two PCs, the difference between several brands was not detected as the distribution of data overlapped with one another. The PCA observation of the elemental composition on PC1 and PC3 has separated the sausage brands into 6 distinctive groups. Besides, several key elements contributed to the brands' identification have been detected, and the most distinctive elements are Na, K, Ca, and Ba. PCA visualization showed the feasibility of the classification of sausage samples from different brands when combined with the results of FT-IR and ICP-MS methods. The experiment was able to differentiate the sausages from the 5 brands using multivariate statistics.

Recyclable Cu-Ag bimetallic nanocatalyst for radical scavenging, dyes removal and antimicrobial applications

An ecofriendly and cost effective green method has been used for the synthesis of recyclable, high functional nanoparticles. Bimetallic nanoparticles (BmNPs), Cu-Ag, have been synthesized using beetroot extract as reducing and capping agent. Formation of BmNPs was initially confirmed by UV-visible analysis, having distinct peaks of Ag at 429 nm and Cu at 628 nm. FTIR analysis also confirmed the association of bioactive phytochemicals with Cu-Ag nanoparticles. Crystallinity and morphology of BmNPs was determined through X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), dynamic light scattering (DLS) and energy dispersion X-ray spectroscopy (EDAX). The size of spherical shape Cu-Ag BmNPs was found to be 75.58 nm and EDAX studies confirmed the percent elemental composition of Cu and Ag in synthesized nanocatalyst. Results of different analysis provided supported evidences regarding the formation of BmNPs. Catalytic potential of BmNPs was tested for the degradation of rhodamine B (Rh-B), methylene blue (MB) and methyl orange (MO) dyes. Cu-Ag BmNPs exhibited outstanding catalytic activity for the degradation of selected organic dyes and percent degradation was recorded more than 90% for each dye. In addition, antiradical property of BmNPs was tested employing DPPH^● and ABTS^●+ assays and it was found to be promising. Synthesized BmNPs also exhibited strong antimicrobial activity against Salmonella typhimurium and Bacillus subtilis. Recyclability of nanoparticles was also evaluated and recovery from dye degradation reaction mixture was successfully achieved. The recovered nanoparticles exhibited same catalytic potential for the degradation of Rh-B. The objective of the current study was to synthesize BmNPs Cu-Ag employing a cost effective green method having promising catalytic, antiradical and antimicrobial potential. Further, BmNPs were reused after recovery from catalytic reactions, proving that BmNPs can be recycled having the same efficiency as that of a freshly prepared Cu-Ag BmNPs.

Magnesium ions reversibly bind to DNA double stranded helix in thin films

Effects of magnesium (Mg²⁺) ions on the stability and structural properties of double-stranded DNA are vitally important for DNA folding and functional behavior. Complementing our previous study on highly hydrated thin films of DNA with sodium counterions, with no buffer (pH ≈ 6) and surrounded with Mg²⁺ cations, here we use Fourier transform infrared spectroscopy and band shape analysis to explore in detail the vibrational signatures of DNA-magnesium interaction in the case when DNA charges are neutralized solely by Mg²⁺ cations, hereafter called MgDNA. Ion atmosphere has been controlled by the magnesium to phosphate molar concentration ratio r which varied between 0.0067 and 10. For r = 0 we find that spectral features in the base region remain similar as in DNA, whereas changes in the backbone region indicate that the B conformation becomes fully stabilized. With increasing r a pronounced structural reshaping occurs in the phosphate backbone region indicating a blue shift of the asymmetric band, while the symmetric band does not show any displacement in frequency. The band shape analysis of overlapping peaks in the respective phosphate regions demonstrates that the number of constituent modes as well as their positions in frequency do not change, whereas their intensities and bandwidths display disparate changes. The results reflect a variety of local environments at the DNA backbone due to a heterogeneous ion atmosphere with randomly distributed magnesium ions and local patterns of hydrogen bonds which change with increasing r. Remarkably, after crowded r = 10 ion atmosphere is depleted, Mg induced spectral changes vanish and structural features of MgDNA (r ≈ 0) are fully restored. Overall results strongly suggest that in MgDNA on highly hydrated thin films the hydrogen-base pairing remains preserved and that Mg²⁺ ions, similar to sodium ions, retain their mobility and interact with double helix via water-mediated electrostatic forces.

Online moisture measurement of dead fine fuel on the forest floor using near-infrared reflectometry

A method for online measurement of the levels of moisture in dead fine fuels on the forest floor is proposed based on near-infrared reflectometry. A linear relationship is found between the moisture content of dead fine fuels and the intensity of light absorption at a wavelength of 1450 nm, and this forms the basic principle of measurement for different forest species. An online moisture-monitoring device is designed and developed based on this principle. This uses long-distance wireless data-transmission to adapt to the lack of telecoms service in remote forests. Moisture-measurement experiments were conducted continuously for 24 h in rainless weather during the summer of 2019 in the Harbin Urban Forestry Demonstration Base of Northeast Forestry University using leaves from four different forest species, specifically, larch, dryland willow, silver birch, and walnut. These measurements were verified using simultaneous measurements employing a weighing method. The results from the field experiments agreed well with the weighing results. The uncertainty in the moisture-content measurements using the proposed system was ∼3.4% when the true moisture content was 20.0% or less. This proposed system has the advantages of online measurement and high accuracy. This method for monitoring moisture levels in dead fine fuels is an important advance in terms of improving the accuracy and simultaneity of measurements for prediction of forest-fire-risk ratings and fire behavior.

Exploration of Principal Component Analysis: Deriving Principal Component Analysis Visually Using Spectra

Spectroscopy rapidly captures a large amount of data that is not directly interpretable. Principal component analysis is widely used to simplify complex spectral datasets into comprehensible information by identifying recurring patterns in the data with minimal loss of information. The linear algebra underpinning principal component analysis is not well understood by many applied analytical scientists and spectroscopists who use principal component analysis. The meaning of features identified through principal component analysis is often unclear. This manuscript traces the journey of the spectra themselves through the operations behind principal component analysis, with each step illustrated by simulated spectra. Principal component analysis relies solely on the information within the spectra, consequently the mathematical model is dependent on the nature of the data itself. The direct links between model and spectra allow concrete spectroscopic explanation of principal component analysis , such as the scores representing "concentration" or "weights". The principal components (loadings) are by definition hidden, repeated and uncorrelated spectral shapes that linearly combine to generate the observed spectra. They can be visualized as subtraction spectra between extreme differences within the dataset. Each PC is shown to be a successive refinement of the estimated spectra, improving the fit between PC reconstructed data and the original data. Understanding the data-led development of a principal component analysis model shows how to interpret application specific chemical meaning of the principal component analysis loadings and how to analyze scores. A critical benefit of principal component analysis is its simplicity and the succinctness of its description of a dataset, making it powerful and flexible.

Tailoring the Functional Potential of Red Beet Purées by Inoculation with Lactic Acid Bacteria and Drying

This study was focused on a comparative analysis of two drying methods, such as convective and infrared drying, on the red beetroot purées with lactic acid bacteria, as a strategy for tailoring the health benefits of the selected plant. For both varieties, the total betalain contents varied from 13.95 ± 0.14 mg/g dry weight in Beta vulgaris var. cylindra when compared with 11.09 ± 0.03 mg/g dry weight in Beta vulgaris var. vulgaris, whereas significant differences were found in total phenolic and flavonoid contents. Significant drying induced changes were found in selected bioactives, in terms of total betalains, flavonoids, and polyphenols, which influenced the antioxidant activities of the purées, structure, and color parameters. In general, infrared technology was more protective, leading to an increase of 20% in flavonoids content. One logarithmic decrease in cell viability was observed in all powders samples. After the in vitro digestion, the betalains decreased, in both gastric and intestinal simulated juices, with a more pronounced profile in infrared processed purées. Textural and rheological analysis of the dried purées highlighted that the infrared drying is milder compared to the conventional one, allowing us to obtain powders with enhanced functional properties, in terms of bioactives content, cell viability, color, and structural and rheological behavior.

Deep learning for FTIR histology: leveraging spatial and spectral features with convolutional neural networks

Current methods for cancer detection rely on tissue biopsy, chemical labeling/staining, and examination of the tissue by a pathologist. Though these methods continue to remain the gold standard, they are non-quantitative and susceptible to human error. Fourier transform infrared (FTIR) spectroscopic imaging has shown potential as a quantitative alternative to traditional histology. However, identification of histological components requires reliable classification based on molecular spectra, which are susceptible to artifacts introduced by noise and scattering. Several tissue types, particularly in heterogeneous tissue regions, tend to confound traditional classification methods. Convolutional neural networks (CNNs) are the current state-of-the-art in image classification, providing the ability to learn spatial characteristics of images. In this paper, we demonstrate that CNNs with architectures designed to process both spectral and spatial information can significantly improve classifier performance over per-pixel spectral classification. We report classification results after applying CNNs to data from tissue microarrays (TMAs) to identify six major cellular and acellular constituents of tissue, namely adipocytes, blood, collagen, epithelium, necrosis, and myofibroblasts. Experimental results show that the use of spatial information in addition to the spectral information brings significant improvements in the classifier performance and allows classification of cellular subtypes, such as adipocytes, that exhibit minimal chemical information but have distinct spatial characteristics. This work demonstrates the application and efficiency of deep learning algorithms in improving the diagnostic techniques in clinical and research activities related to cancer.

Qualitative Analysis of Traditional Italian Dishes: FTIR Approach

Italian cuisine and its traditional recipes experience an ever-increasing popularity around the world. The “Integrated Approach” is the key to modern food research and the innovative challenge for analyzing and modeling agro-food systems in their totality. The present study aims at applying and evaluating Fourier Transformed Infrared (FTIR) spectroscopy for the analysis of complex food matrices and food preparations. Nine traditional Italian recipes, including First courses, One-dish meals, Side courses, and Desserts, were selected and experimentally prepared. Prior to their analysis via FTIR spectroscopy, the samples were homogenized and lyophilized. The IR spectroscopic characterization and the assignment of the main bands was carried out. Numerous peaks, which correspond to functional groups and modes of vibration of the individual components, were highlighted. The spectra are affected by both the preparation procedures, the cooking methods, and the cooking time. The qualitative analysis of the major functional groups can serve as a basis for a discrimination of the products and the investigation of fraud. For this purpose, the FTIR spectra were evaluated using Principal Component Analysis (PCA). Our results show how the utilization of vibrational spectroscopy combined with a well-established chemometric data analysis method represents a potentially powerful tool in research linked to the food sector and beyond. This study is a first step towards the development of new indicators of food quality.