Response of Ovalbumin to Fructose Addition and pH Variations - Ultrasonic and FTIR Study

Main aim of this work is to understand how the protein ovalbumin is affected by the presence of cosolvent and variations in pH of the medium. The addition of cosolvent in many cases is found to control the extent of denaturation and pH is one of the main sources of denaturant of proteins. In this work, keeping fructose solution as cosolvent and pH of the solution as main variable, the extent of denaturation is analysed by ultrasonic methods and are further confirmed by FTIR amide-I second derivative spectra at 303 K. Obtained results shows that denaturation is sensitive to pH, however, acidic and alkaline behave totally in a different way. It was found that the impact of alkaline pH produces lesser denaturation and is slower whereas the impact of acidic pH is specific and instantaneous. Ultrasonic analysis shows that pH variation can denature the protein whereas the addition of cosolvent supports renaturation. FTIR spectra were recorded for the experimental samples from which the second derivative curve fitted spectra were constructed using Origin program. Quantitative assignment of peaks and the variations in cumulative areas calculated for the structures like α-helix, β-sheets etc confirms the observations of ultrasonic analysis that the pH variations aid in denaturation whereas the cosolvent supports the renaturation of protein.

Alternative Methods for Measuring the Susceptibility of White Wines to Pinking Alteration: Derivative Spectroscopy and CIEL*a*b* Colour Analysis

Pinking is the term used for describing the pink colouration that appears in white wines produced under reducing conditions when oxidised. The ability to predict the susceptibility of white wines for pinking is of utmost importance for wine producers. In this work, we critically compare the two most currently used methods for measuring pinking susceptibility and the use of the first derivative spectra and the CIEL*a*b* colour space method. The amplitude of the first derivative spectra in the 450–550 nm range has a good correlation with the values obtained by subtracting the extrapolate background at 500 nm (R2 = 0.927); therefore, first derivative spectroscopy seems to be a more straightforward approach for eliminating the background problem that occurs in this method. The CIEL*a*b* method using the a* value after oxidation seems to be the most appropriate method to measure the pinking susceptibility of white wines, showing a very good correlation with the amplitude of the first derivative spectra. The pink colouration visualisation is linearly related to the b* value of the white wine, showing that no universal cut-off value for predicting the pink visualisation should be used. Second derivative spectra allow the observation of the formation of different chromophores in wines after induced oxidation.

Response of Ovalbumin to Fructose Addition and pH Variations - Ultrasonic and FTIR Study

Main aim of this work is to understand how the protein ovalbumin is affected by the presence of cosolvent and variations in pH of the medium. The addition of cosolvent in many cases is found to control the extent of denaturation and pH is one of the main sources of denaturant of proteins. In this work, keeping fructose solution as cosolvent and pH of the solution as main variable, the extent of denaturation is analysed by ultrasonic methods and are further confirmed by FTIR amide-I second derivative spectra at 303 K. Obtained results shows that denaturation is sensitive to pH, however, acidic and alkaline behave totally in a different way. It was found that the impact of alkaline pH produces lesser denaturation and is slower whereas the impact of acidic pH is specific and instantaneous. Ultrasonic analysis shows that pH variation can denature the protein whereas the addition of cosolvent supports renaturation. FTIR spectra were recorded for the experimental samples from which the second derivative curve fitted spectra were constructed using Origin program. Quantitative assignment of peaks and the variations in cumulative areas calculated for the structures like α-helix, β-sheets etc confirms the observations of ultrasonic analysis that the pH variations aid in denaturation whereas the cosolvent supports the renaturation of protein.

Comparison of partial least squares-discriminant analysis, support vector machines and deep neural networks for spectrometric classification of seed vigour in a broad range of tree species

Seed vigour significantly influences the seed production and plant regeneration performance. The capability of NIR spectroscopy to identify seed vigour across multiple tree species rapidly and cost-effectively has been examined. The NIR spectra of seeds from five different tree species have been taken. Standard germination testing has also been used to verify seed vigour. Three classification models were trained, i.e., partial least squares-discriminant analysis (PLSDA), support vector machine (SVM) and Multilayer Deep neural network (DNN). Three types of spectral pre-processing methods and their combination were used to fit for the best classification model. The DNN model has shown good performance on all pre-processing methods and yielded higher accuracy than other models in this study, with accuracy, sensitivity, precision and specificity all equal to 1. Compared with other pre-processing methods, the second derivative spectra have shown a robust and consistent classification result in both PLSDA and DNN models. Five important regions including 1270, 1650, 1720, 2100, 2300 nm were found highly related to the seed vigour. This study has found a rapid and efficient methodology for seed vigour classification, which could serve for industrial use in a rapid and non-destructive way.

The separation of softwood and hardwood in historical wooden statues of the Nazenji-temple in Japan using NIR spectroscopy

The applicability of near-infrared (NIR) spectroscopy to the identification of wood species of archaeologically/historically valuable wooden artifacts in a non-invasive manner was investigated using reference wood samples from the xylarium of the Forestry and Forest Products Research Institute (TWTw) and applied to several wooden statues carved about 1000 years ago. Diffuse-reflectance NIR spectra were obtained from five standard wood samples each of five softwood species (Chamaecyparis obtusa, Cryptomeria japonica, Sciadopitys verticillata, Thujopsis dolabrata, Torreya nucifera) and five hardwood species (Aesculus turbinata, Cercidiphyllum japonicum, Cinnamomum camphora, Prunus jamasakura, Zelkova serrata). A principal component analysis (PCA) model was developed from the second derivative spectra. The score plot of the first two components clearly showed separation of the wood sample data into softwood and hardwood clusters. The developed PCA model was applied to 370 spectra collected from 21 wooden statues preserved in the Nazenji-temple in Shizuoka Prefecture in Japan, including 14 made from Torreya spp. and 7 made from Cinnamomum spp. In the score plot, the statue spectra were also divided into two clusters, corresponding to either softwood (Torreya spp.) or hardwood (Cinnnamomum spp.) species. These results show that NIR spectroscopy combined with PCA is a powerful technique for determining whether archaeologically/historically valuable wooden artifacts are made of softwood or hardwood.

Application of neural networks for classifying softwood species using near infrared spectroscopy

Lumber species identification is an important issue for the wood industry. In this study, three types of neural networks (artificial neural network (ANN), deep neural network (DNN), and convolutional neural network (CNN)) were employed for classifying softwood lumber species using NIR spectroscopy. The results show that CNN, which is based on deep learning, was more stable than the other neural networks. In particular, the stability of the training process was remarkably improved in CNN models. During the training procedure, the validation accuracy of the CNN model was 99.3% for the raw spectra, 99.9% for the standard normal variate (SNV) spectra and 100.0% for the Savitzky-Golay second derivative spectra. Interestingly, there was little difference in the validation accuracies among the CNN models depending on mathematical preprocessing. The results showed that CNN is sufficiently adequate to classify the softwood lumber species.

A Simple, Label-Free, and High-Throughput Method to Evaluate the Epigallocatechin-3-Gallate Impact in Plasma Molecular Profile

Epigallocatechin-3-gallate (EGCG), the major catechin present in green tea, presents diverse appealing biological activities, such as antioxidative, anti-inflammatory, antimicrobial, and antiviral activities, among others. The present work evaluated the impact in the molecular profile of human plasma from daily consumption of 225 mg of EGCG for 90 days. Plasma from peripheral blood was collected from 30 healthy human volunteers and analyzed by high-throughput Fourier transform infrared spectroscopy. To capture the biochemical information while minimizing the interference of physical phenomena, several combinations of spectra pre-processing methods were evaluated by principal component analysis. The pre-processing method that led to the best class separation, that is, between the plasma spectral data collected at the beginning and after the 90 days, was a combination of atmospheric correction with a second derivative spectra. A hierarchical cluster analysis of second derivative spectra also highlighted the fact that plasma acquired before EGCG consumption presented a distinct molecular profile after the 90 days of EGCG consumption. It was also possible by partial least squares regression discriminant analysis to correctly predict all unlabeled plasma samples (not used for model construction) at both timeframes. We observed that the similarity in composition among the plasma samples was higher in samples collected after EGCG consumption when compared with the samples taken prior to EGCG consumption. Diverse negative peaks of the normalized second derivative spectra, associated with lipid and protein regions, were significantly affected (p < 0.001) by EGCG consumption, according to the impact of EGCG consumption on the patients’ blood, low density and high density lipoproteins ratio. In conclusion, a single bolus dose of 225 mg of EGCG, ingested throughout a period of 90 days, drastically affected plasma molecular composition in all participants, which raises awareness regarding prolonged human exposure to EGCG. Because the analysis was conducted in a high-throughput, label-free, and economic analysis, it could be applied to high-dimension molecular epidemiological studies to further promote the understanding of the effect of bio-compound consumption mode and frequency.

Soil Color and Mineralogy Mapping Using Proximal and Remote Sensing in Midwest Brazil

Soil color and mineralogy are used as diagnostic criteria to distinguish different soil types. In the literature, 350–2500 nm spectra were successfully used to predict soil color and mineralogy, but these attributes currently are not mapped for most Brazilian soils. In this paper, we provided the first large-extent maps with 30 m resolution of soil color and mineralogy at three depth intervals for 850,000 km2 of Midwest Brazil. We obtained soil 350–2500 nm spectra from 1397 sites of the Brazilian Soil Spectral Library at 0–20 cm, 20–60, and 60–100 cm depths. Spectra was used to derive Munsell hue, value, and chroma, and also second derivative spectra of the Kubelka–Munk function, where key spectral bands were identified and their amplitude measured for mineral quantification. Landsat composites of topsoil and vegetation reflectance, together with relief and climate data, were used as covariates to predict Munsell color and Fe–Al oxides, and 1:1 and 2:1 clay minerals of topsoil and subsoil. We used random forest for soil modeling and 10-fold cross-validation. Soil spectra and remote sensing data accurately mapped color and mineralogy at topsoil and subsoil in Midwest Brazil. Hematite showed high prediction accuracy (R2 > 0.71), followed by Munsell value and hue. Satellite topsoil reflectance at blue spectral region was the most relevant predictor (25% global importance) for soil color and mineralogy. Our maps were consistent with pedological expert knowledge, legacy soil observations, and legacy soil class map of the study region.

Application of Near-Infrared Hyperspectral Imaging with Machine Learning Methods to Identify Geographical Origins of Dry Narrow-Leaved Oleaster (Elaeagnus angustifolia) Fruits

Narrow-leaved oleaster (Elaeagnus angustifolia) fruit is a kind of natural product used as food and traditional medicine. Narrow-leaved oleaster fruits from different geographical origins vary in chemical and physical properties and differ in their nutritional and commercial values. In this study, near-infrared hyperspectral imaging covering the spectral range of 874–1734 nm was used to identify the geographical origins of dry narrow-leaved oleaster fruits with machine learning methods. Average spectra of each single narrow-leaved oleaster fruit were extracted. Second derivative spectra were used to identify effective wavelengths. Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were used to build discriminant models for geographical origin identification using full spectra and effective wavelengths. In addition, deep convolutional neural network (CNN) models were built using full spectra and effective wavelengths. Good classification performances were obtained by these three models using full spectra and effective wavelengths, with classification accuracy of the calibration, validation, and prediction set all over 90%. Models using effective wavelengths obtained close results to models using full spectra. The performances of the PLS-DA, SVM, and CNN models were close. The overall results illustrated that near-infrared hyperspectral imaging coupled with machine learning could be used to trace geographical origins of dry narrow-leaved oleaster fruits.

Differential Molecular Signature of Human Saliva Using ATR-FTIR Spectroscopy for Chronic Kidney Disease Diagnosis

The symptoms of chronic kidney disease (CKD) are often not specific or absent in the early stages of this illness. Therefore, there is a demand for developing low cost, non-invasive and highly accurate platforms for CKD diagnostics. We hypothesized that the level of specifics salivary components changes when CKD is emplace, which could be clinically used to discriminate CKD patients from healthy subjects. The present study aimed to compare salivary components between CKD patients and matched control subjects by using attenuated total reflection-Fourier Transform Infrared (ATR-FTIR) spectroscopy. The predictive power of salivary components was evaluated by receiver operating characteristic (ROC) curves. Several components were identified, and 4 of them showed different expression (p<0.05) between CKD and control subjects. Thiocyanate (SCN-, 2052 cm-1) and phospholipids/carbohydrates (924 cm-1) vibrational modes using original and second-derivative spectra by ATR-FTIR could potentially be used as salivary biomarkers to differentiate CKD than control subjects. The combination of original and second-derivative spectra by ATR-FTIR of 924 cm-1 vibrational modes could reach 92.8% sensitivity and 85.7% specificity for CKD detection. Despite, the limitation of our investigation, the acquired data indicates that salivary vibrational modes by ATR-FTIR platform should be further explored as an auxiliary diagnostic tool for CKD.