Raman Molecular Fingerprints of Rice Nutritional Quality and the Concept of Raman Barcode

Quantitative prediction of rice starch digestibility using Raman spectroscopy and multivariate calibration analysis

Digestibility is an important characteristic of rice starch. It is affected by the growing environment, such as temperature and soil, so that even in the same genetic cultivar the digestibility of each product will be different. Here, we predicted rice starch digestibility by Raman scattering spectroscopy. A partial least squares (PLS) regression analysis was performed between biochemically quantified digestibility index values and Raman scattering spectra of purified starch from rice samples of different cultivars and growing conditions. The prediction model obtained by analyzing the individual cultivars was able to predict digestibility with a high accuracy, with an R2 of 0.95 and RMSEP of 0.43, whereas a mixture of all cultivars resulted in more than two times worse accuracy. Our finding suggests that the molecular structures affecting digestibility fluctuate depending on the growing environment while maintaining a unique balance regulated by cultivar-specific starch synthesis mechanisms.

Raman Fingerprints of SARS-CoV-2 Omicron Subvariants: Molecular Roots of Virological Characteristics and Evolutionary Directions

The latest RNA genomic mutation of SARS-CoV-2 virus, termed the Omicron variant, has generated a stream of highly contagious and antibody-resistant strains, which in turn led to classifying Omicron as a variant of concern. We systematically collected Raman spectra from six Omicron subvariants available in Japan (i.e., BA.1.18, BA.2, BA.4, BA.5, XE, and BA.2.75) and applied machine-learning algorithms to decrypt their structural characteristics at the molecular scale. Unique Raman fingerprints of sulfur-containing amino acid rotamers, RNA purines and pyrimidines, tyrosine phenol ring configurations, and secondary protein structures clearly differentiated the six Omicron subvariants. These spectral characteristics, which were linked to infectiousness, transmissibility, and propensity for immune evasion, revealed evolutionary motifs to be compared with the outputs of genomic studies. The availability of a Raman "metabolomic snapshot", which was then translated into a barcode to enable a prompt subvariant identification, opened the way to rationalize in real-time SARS-CoV-2 activity and variability. As a proof of concept, we applied the Raman barcode procedure to a nasal swab sample retrieved from a SARS-CoV-2 patient and identified its Omicron subvariant by coupling a commercially available magnetic bead technology with our newly developed Raman analyses.

Raman Multi-Omic Snapshot and Statistical Validation of Structural Differences between Herpes Simplex Type I and Epstein-Barr Viruses

Raman spectroscopy was applied to study the structural differences between herpes simplex virus Type I (HSV-1) and Epstein-Barr virus (EBV). Raman spectra were first collected with statistical validity on clusters of the respective virions and analyzed according to principal component analysis (PCA). Then, average spectra were computed and a machine-learning approach applied to deconvolute them into sub-band components in order to perform comparative analyses. The Raman results revealed marked structural differences between the two viral strains, which could mainly be traced back to the massive presence of carbohydrates in the glycoproteins of EBV virions. Clear differences could also be recorded for selected tyrosine and tryptophan Raman bands sensitive to pH at the virion/environment interface. According to the observed spectral differences, Raman signatures of known biomolecules were interpreted to link structural differences with the viral functions of the two strains. The present study confirms the unique ability of Raman spectroscopy for answering structural questions at the molecular level in virology and, despite the structural complexity of viral structures, its capacity to readily and reliably differentiate between different virus types and strains.

Raman Multi-Omic Snapshots of Koshihikari Rice Kernels Reveal Important Molecular Diversities with Potential Benefits in Healthcare

This study exploits quantitative algorithms of Raman spectroscopy to assess, at the molecular scale, the nutritional quality of individual kernels of the Japanese short-grain rice cultivar Koshihikari in terms of amylose-to-amylopectin ratio, fractions of phenylalanine and tryptophan aromatic amino acid residues, protein-to-carbohydrate ratio, and fractions of protein secondary structures. Statistical assessments on a large number of rice kernels reveal wide distributions of the above nutritional parameters over nominally homogeneous kernel batches. This demonstrates that genetic classifications cannot catch omic fluctuations, which are strongly influenced by a number of extrinsic factors, including the location of individual grass plants within the same rice field and the level of kernel maturation. The possibility of collecting nearly real-time Raman "multi-omic snapshots" of individual rice kernels allows for the automatic (low-cost) differentiation of groups of kernels with restricted nutritional characteristics that could be used in the formulation of functional foods for specific diseases and in positively modulating the intestinal microbiota for protection against bacterial infection and cancer prevention.

Nondestructive testing methods for pesticide residue in food commodities: A review

Pesticides play an important role in increasing the overall yield and productivity of agricultural foods by controlling pests, insects, and numerous plant-related diseases. However, the overuse of pesticides has resulted in pesticide contamination of food products and water bodies, as well as disruption of ecological and environmental systems. Global health authorities have set limits for pesticide residues in individual food products to ensure the availability of safe foods in the supply system and to assist farmers in developing the best agronomic practices for crop production. Therefore, the use of nondestructive testing (NDT) methods for pesticide residue detection is gaining interest in the food supply chain. The NDT techniques have several advantages, such as simultaneous measurement of chemical and physical characteristics of food without destroying the product. Although numerous studies have been conducted on NDT for pesticide residue in agro-food products, there are still challenges in real-time implementation. Further study on NDT methods is needed to establish their potential for supplementing existing methods, identifying mixed pesticides, and performing volumetric quantification (not surface accumulation alone).

Raman spectroscopy detects chemical differences between potato tubers produced under normal and heat stress growing conditions

Potato is the most consumed vegetable worldwide. Potato tubers contain water, starch, proteins, minerals, and vitamins. The amounts of these chemicals depend on the cultivar and growing location. When potatoes are exposed to high temperatures during the growing period, tuber yield and quality are detrimentally affected; however, there is limited knowledge about the influence of high temperatures on tuber chemical composition. With temperatures rising around the globe, the reaction of potato cultivars to high temperatures is increasingly important, and heat-induced changes, including changes in the chemical composition of tubers, should be considered. The Texas A&M University Potato Breeding Program has been selecting potato clones under high-temperature conditions for many years. Several released cultivars are considered heat-tolerant based on high marketable yields and low internal and external tuber defects. In this study, we used Raman spectroscopy (RS), an analytical tool, to determine whether heat stress causes changes in the chemical composition of tubers of ten potato cultivars. RS is a non-invasive method that requires less time and labor than conventional chemical analysis. We found drastic changes in the intensities of vibrational bands that originate from carbohydrates in the spectra acquired from tubers of heat-stressed plants compared to tubers produced by potato plants grown under normal conditions. These results demonstrate that RS could be used as a replacement or complement to conventional chemical analysis to inspect the effect of heat stress on tuber chemical composition.

Surface Chemical and Morphological Analysis of Chitosan/1,3-β-d-Glucan Polysaccharide Films Cross-Linked at 90 °C

The cross-linking temperature of polymers may affect the surface characteristics and molecular arrangement, which are responsible for their mechanical and physico-chemical properties. The aim of this research was to determine and explain in detail the mechanism of unit interlinkage of two-component chitosan/1,3-β-d-glucan matrices gelled at 90 °C. This required identifying functional groups interacting with each other and assessing surface topography providing material chemical composition. For this purpose, various spectroscopic and microscopic approaches, such as attenuated total reflection Fourier transform infrared spectroscopy (ATR FT-IR), Raman spectroscopy, X-ray photoelectron spectroscopy (XPS) and atomic force microscopy (AFM), were applied. The results indicate the involvement mainly of the C-C and C-H groups and C=O⋯HN moieties in the process of biomaterial polymerization. Strong chemical interactions and ionocovalent bonds between the N-glucosamine moieties of chitosan and 1,3-β-d-glucan units were demonstrated, which was also reflected in the uniform surface of the sample without segregation. These unique properties, hybrid character and proper cell response may imply the potential application of studied biomaterial as biocompatible scaffolds used in regenerative medicine, especially in bone restoration and/or wound healing.

Effects of Altitudinal Gradient on Physicochemical and Rheological Potential of Quinoa Cultivars

The protein, carbohydrate, and fat characteristics of quinoa grains reflect in their techno-functional potential. This aspect has been little studied in quinoa, while some physicochemical and rheological characteristics have been generalized for all cultivars under all primary production conditions. The aim of this research is to determine the agro-industrial potential of different quinoa cultivars evaluated under different environments through physicochemical and rheological responses. This study has a factorial design with a first level corresponding to cultivars and a second level to production zones. The results showed that the cultivars present high compositional variability. It was also found that the altitudinal gradient changes protein and starch composition, protein secondary structure, and starch structural conformation. In addition, significant variations were found in viscosity, breakdown, and dispersion setback for all treatments. However, there were no differences between treatments before heating/cooling and after heat treatment.

Intelligent Identification and Features Attribution of Saline–Alkali-Tolerant Rice Varieties Based on Raman Spectroscopy

Planting rice in saline–alkali land can effectively improve saline–alkali soil and increase grain yield, but traditional identification methods for saline–alkali-tolerant rice varieties require tedious and time-consuming field investigations based on growth indicators by rice breeders. In this study, the Python machine deep learning method was used to analyze the Raman molecular spectroscopy of rice and assist in feature attribution, in order to study a fast and efficient identification method of saline–alkali-tolerant rice varieties. A total of 156 Raman spectra of four rice varieties (two saline–alkali-tolerant rice varieties and two saline–alkali-sensitive rice varieties) were analyzed, and the wave crests were extracted by an improved signal filtering difference method and the feature information of the wave crest was automatically extracted by scipy.signal.find_peaks. Select K Best (SKB), Recursive Feature Elimination (RFE) and Select F Model (SFM) were used to select useful molecular features. Based on these feature selection methods, a Logistic Regression Model (LRM) and Random Forests Model (RFM) were established for discriminant analysis. The experimental results showed that the RFM identification model based on the RFE method reached a higher recognition rate of 89.36%. According to the identification results of RFM and the identification of feature attribution materials, amylum was the most significant substance in the identification of saline–alkali-tolerant rice varieties. Therefore, an intelligent method for the identification of saline–alkali-tolerant rice varieties based on Raman molecular spectroscopy is proposed.

Tailoring Vibrational Signature and Functionality of 2D-Ordered Linear-Chain Carbon-Based Nanocarriers for Predictive Performance Enhancement of High-End Energetic Materials

A recently proposed, game-changing transformative energetics concept based on predictive synthesis and preprocessing at the nanoscale is considered as a pathway towards the development of the next generation of high-end nanoenergetic materials for future multimode solid propulsion systems and deep-space-capable small satellites. As a new door for the further performance enhancement of transformative energetic materials, we propose the predictive ion-assisted pulse-plasma-driven assembling of the various carbon-based allotropes, used as catalytic nanoadditives, by the 2D-ordered linear-chained carbon-based multicavity nanomatrices serving as functionalizing nanocarriers of multiple heteroatom clusters. The vacant functional nanocavities of the nanomatrices available for heteroatom doping, including various catalytic nanoagents, promote heat transfer enhancement within the reaction zones. We propose the innovative concept of fine-tuning the vibrational signatures, functionalities and nanoarchitectures of the mentioned nanocarriers by using the surface acoustic waves-assisted micro/nanomanipulation by the pulse-plasma growth zone combined with the data-driven carbon nanomaterials genome approach, which is a deep materials informatics-based toolkit belonging to the fourth scientific paradigm. For the predictive manipulation by the micro- and mesoscale, and the spatial distribution of the induction and energy release domains in the reaction zones, we propose the activation of the functionalizing nanocarriers, assembled by the heteroatom clusters, through the earlier proposed plasma-acoustic coupling-based technique, as well as by the Teslaphoresis force field, thus inducing the directed self-assembly of the mentioned nanocarbon-based additives and nanocarriers.

Raman Fingerprints of Rice Nutritional Quality: A Comparison between Japanese Koshihikari and Internationally Renowned Cultivars

Raman spectroscopy was applied to characterize at the molecular scale the nutritional quality of the Japanese Koshihikari rice cultivar in comparison with other renowned rice cultivars including Carnaroli from Italy, Calrose from the USA, Jasmine rice from Thailand, and Basmati from both India and Pakistan. For comparison, two glutinous (mochigome) cultivars were also investigated. Calibrated and validated Raman analytical algorithms allowed quantitative determinations of: (i) amylopectin and amylose concentrations, (ii) fractions of aromatic amino acids, and (iii) protein content and secondary structure. The Raman assessments non-destructively linked the molecular composition of grains to key nutritional parameters and revealed a complex intertwine of chemical properties. The Koshihikari cultivar was rich in proteins (but with low statistical relevance as compared to other investigated cultivars) and aromatic amino acids. However, it also induced a clearly higher glycemic impact as compared to long-grain cultivars from Asian countries. Complementary to genomics and wet-chemistry analyses, Raman spectroscopy makes non-destructively available factual and data-driven information on rice nutritional characteristics, thus providing customers, dietitian nutritionists, and producers with a solid science-consolidated platform.