Improving Raman spectroscopic identification of rice varieties by feature extraction

Abstract

Raman spectroscopy combined with pattern recognition can identify rice varieties excellently. In this study, a method was established to select the key feature for identification model. Seventy‐two Raman spectra of three varieties of rice were analyzed. Seventy‐one independent variables and characteristic bands (420–560, 820–980, 1,000–1,200, and 1,300–1,500 cm−1) were obtained by principal component analysis (PCA). Window analysis further narrowed the range of characteristic bands (451–550, 951–1,000, and 1,351–1,450 cm−1). Hierarchical cluster analysis (HCA) obtained 30 wavenumbers with small correlation. The prediction accuracy was 91.71%, whereas the time was reduced by 10 times when these 30 wavenumbers were used to establish the identification model. The method combined PCA, window analysis, and HCA with support vector machine can be used as an effective feature extraction method to improve the efficiency for identification of rice varieties. Under the circumstances of large sample size or relatively complex data, the screening of Raman spectrum information is an important means to simplify the model and improve the prediction efficiency.

Clustering Honey Samples with Unsupervised Machine Learning Methods using FTIR Data

“…Euclidean distance is one of them. M.Sha et al reported that Euclidean distance gave the best results in their studies based on raman spectroscopy [42]. Looking at Fig.…”

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

“…Food technologists have not yet reached a consensus on quality classes and measurement methods, so that quantitative indicators and routine metrics for addressing raw-grain quality at the molecular scale are yet conspicuously missing. In this context, Raman spectroscopy offers a unique chance for fast, quantitative, and non-destructive evaluations of rice nutritional value in real time and at low cost [ 5 , 6 ]. Being a “fingerprint” of molecular composition, the Raman spectrum embodies a large amount of structural information.…”

Viscothermal Behavior and Structural Characterization of Temperate Highland Himalayan Rice Cultivars

“…Sha et al. [ 15 ] distinguished 72 rice varieties using Raman spectroscopy in combination with pattern recognition and reported that protein and starch were the key components that could be used to distinguish the rice varieties at micromolecular level. However, their applications have remained so far very limited for varieties grown under highland Himalayan conditions.…”

“…Various instrumental techniques can be used to characterize and differentiate starch and other associated components in different varieties of rice. Differential scanning colorimetry (DSC), X-ray diffraction (XRD), and rapid visco analyzer (RVA) can be used to investigate the gelatinization behavior of starches; [13] Fourier transform infrared spectroscopy (FTIR) is a rapid technique, which can be used to identify the functional groups of the molecules; [14] Raman spectroscopy provides information required to elucidate the structure and has better structural selectivity than other spectroscopic techniques, [15] while granular morphology of starch and other associated components can be accessed through scanning electron microscopy (SEM). Shujka et al [14] correlated the content of various nutritional components present in commercial flour samples by both classic chemical methods and FTIR spectroscopy.…”

“…The results obtained confirmed that application of FTIR spectral data is accurate, rapid, and convenient methodology for determination of flour characteristics, as well as for the detection of selected flour ingredients. Sha et al [15] distinguished 72 rice varieties using Raman spectroscopy in combination with pattern recognition and reported that protein and starch were the key components that could be used to distinguish the rice varieties at micromolecular level. However, their applications have remained so far very limited for varieties grown under highland Himalayan conditions.…”