Raman spectroscopic analysis of polysaccharides in popular Japanese rice cultivars

Portable Raman spectroscopy coupled with PLSR analysis for monitoring and predicting of the quality of fresh-cut Chinese yam at different storage temperatures

Portable Raman spectroscopy coupled with partial least squares regression (PLSR) model was performed for monitoring and predicting four quality indicators, moisture content, water activity, polysaccharide content and microbial content of the fresh-cut Chinese yam at different storage temperatures. The variations in the four key indicators were first depicted through a spider web diagram as the storage temperature changed. More importantly, the four key indicators can be accurately monitored and predicted through optimized PLSR models combining with Raman spectroscopy. Among all of the PLSR models for the four indicators, the regression model for moisture content was relatively the best. In addition, storage temperature played a significant role on the model performance of PLSR. The model performance for all indicators at room temperature and high temperature was better than the corresponding PLSR models at refrigeration and freezing conditions. Especially at 25 ℃, the R2 in the calibration set basically reached 0.9. These observations indicated that portable Raman spectroscopy, a simple and easy-to-use detection technique, can monitor and predict the multiple quality indicators of fresh-cut Chinese yam combined with effectively PLSR model, which would be conducive to their applications in food industry.

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.

Structural analysis of potato starch transformation during high-energy ball-milling: Oxygen and humidity content effects

High Energy Ball-Milling (HEBM) modifies starchs' granule morphology, physicochemical properties, and chemical structure. However, understanding how the HEBM changes the starch chemical structure is necessary to control these modifications. Therefore, this study aimed to investigate the changes in potato starch's long- and short-range molecular order during HEBM at different environmental conditions such as oxygen (Air) and humidity content. Due to the correlation between the starch modification and the energy supplied (Esupp) by the HEBM, Burgio's equation was used to calculate this energy. The starch transformation was followed by X-ray diffraction, Fourier Transform-Infrared Spectroscopy, and Raman spectroscopy. A Principal Component Analysis (PCA) was conducted to reduce the HEBM variables. PAC analysis demonstrated that the different oxygen-humidity conditions do not affect the HEBM of potato starch. Based on the starch chemical structure transformation correlated with Esupp during HEBM, four stages were observed: orientation, modification, mechanolysis, and over-destruction. It was identified for the first time that at low milling energy (<1.5 kJ/g, orientation stage), the glycosidic rings change their orientation, and starch-water interaction increases while the starch's organization reduces. Ergo, the potato starch could be more susceptible to chemical modifications during the first two stages.

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.

Rapid discrimination of glycogen particles originated from different eukaryotic organisms

There are many commercially available glycogen particles in the market due to their bioactive functions as food additive, drug carrier and natural moisturizer, etc. It would be beneficial to rapidly determine the origins of commercially-available glycogen particles, which could facilitate the establishment of quality control methodology for glycogen-containing products. With its non-destructive, label-free and low-cost features, surface enhanced Raman spectroscopy (SERS) is an attractive technique with high potential to discriminate chemical compounds in a rapid mode. In this study, we applied the combination of SERS technique and machine leaning algorithms on glycogen analysis, which successfully predicted the origins of glycogen particles from a variety of organisms with convolutional neural network (CNN) algorithm plus attention mechanism having the best computational performance (5-fold cross validation accuracy = 96.97 %). In sum, this is the first study focusing on the discrimination of commercial glycogen particles originated from different organisms, which holds the application potential in quality control of glycogen-containing products.

Intelligent Classification of Japonica Rice Growth Duration (GD) Based on CapsNets

Rice cultivation in cold regions of China is mainly distributed in Heilongjiang Province, where the growing season of rice is susceptible to low temperature and cold damage. Choosing and planting rice varieties with suitable GD according to the accumulated temperate zone is an important measure to prevent low temperature and cold damage. However, the traditional identification method of rice GD requires lots of field investigations, which are time consuming and susceptible to environmental interference. Therefore, an efficient, accurate, and intelligent identification method is urgently needed. In response to this problem, we took seven rice varieties suitable for three accumulated temperature zones in Heilongjiang Province as the research objects, and we carried out research on the identification of japonica rice GD based on Raman spectroscopy and capsule neural networks (CapsNets). The data preprocessing stage used a variety of methods (signal.filtfilt, difference, segmentation, and superposition) to process Raman spectral data to complete the fusion of local features and global features and data dimension transformation. A CapsNets containing three neuron layers (one convolutional layer and two capsule layers) and a dynamic routing protocol was constructed and implemented in Python. After training 160 epochs on the CapsNets, the model achieved 89% and 93% accuracy on the training and test datasets, respectively. The results showed that Raman spectroscopy combined with CapsNets can provide an efficient and accurate intelligent identification method for the classification and identification of rice GD in Heilongjiang Province.

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.

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.

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

The nutritional quality of rice is contingent on a wide spectrum of biochemical characteristics, which essentially depend on rice genome, but are also greatly affected by growing/environmental conditions and aging during storage. The genetic basis and related identification of genes have widely been studied and rationally linked to accumulation of micronutrients in grains. However, genetic classifications cannot catch quality fluctuations arising from interannual, environmental, and storage conditions. Here, we propose a quantitative spectroscopic approach to analyze rice nutritional quality based on Raman spectroscopy, and disclose analytical algorithms for the determination of: (i) amylopectin and amylose concentrations, (ii) aromatic amino acids, (iii) protein content and structure, and (iv) chemical residues. The proposed Raman algorithms directly link to the molecular composition of grains and allow fast/non-destructive determination of key nutritional parameters with minimal sample preparation. Building upon spectroscopic information at the molecular level, we newly propose to represent the nutritional quality of labeled rice products with a barcode specially tailored on the Raman spectrum. The Raman barcode, which can be stored in databases promptly consultable with barcode scanners, could be linked to diet applications (apps) to enable a rapid, factual, and unequivocal product identification based on direct molecular screening.