Nutrient content prediction and geographical origin identification of red raspberry fruits by combining hyperspectral imaging with chemometrics

Abstract

The geographical origin and the important nutrient contents greatly affect the quality of red raspberry (RRB, Rubus idaeus L.), a popular fruit with various health benefits. In this study, a chemometrics-assisted hyperspectral imaging (HSI) method was developed for predicting the nutrient contents, including pectin polysaccharides (PPS), reducing sugars (RS), total flavonoids (TF) and total phenolics (TP), and identifying the geographical origin of RRB fruits. The results showed that these nutrient contents in RRB fruits had significant differences between regions (P < 0.05) and could be well predicted based on the HSI full or effective wavelengths selected through competitive adaptive reweighted sampling (CARS) and variable iterative space shrinkage approach (VISSA). The best prediction results of PPS, RS, TF, and TP contents were achieved with the highest residual predictive deviation (RPD) values of 3.66, 3.95, 2.85, and 4.85, respectively. The RRB fruits from multi-regions in China were effectively distinguished by using the first derivative-partial least squares discriminant analysis (DER-PLSDA) model, with an accuracy of above 97%. Meanwhile, the fruits from three protected geographical indication (PGI) regions were successfully classified by using the orthogonal partial least squares discrimination analysis (OPLSDA) model, with an accuracy of above 98%. The study results indicate that HSI assisted with chemometrics is a promising method for predicting the important nutrient contents and identifying the geographical origin of red raspberry fruits.

Research Progress on Extraction and Detection Technologies of Flavonoid Compounds in Foods

“…Compared with the NIR spectrum obtained from a single point, HSI can obtain the spectrum of each pixel in the image at a specific wavelength or spectral region, making it feasible to predict the physical characteristics and chemical composition of a single pixel and obtain more representative spectral information [112]. Wang et al [113] developed a stoichiometric-assisted HSI method to predict nutrient composition, including pectin polysaccharides (PPS), reducing sugars (RS), total flavonoids (TF), and total phenols (TP), enabling geographical origin differentiation of red raspberry. Zhang et al [114] determined the total anthocyanins, total flavonoids, and total phenols in dry black goji berries using near-infrared hyperspectral imaging.…”

Origin traceability and adulteration detection of soybean using near infrared hyperspectral imaging

“…The different qualities of green tea related to different production areas were determined successfully with NIR‐HSI (Liu et al., 2022). Moreover, orthogonal PLS‐DA model showed great potential to distinguish the red raspberry of three protected geographical indication regions (Wang et al., 2022). Multitask residual fully convolutional network under Bayesian optimized architecture was used to build the near four regions of Ningxia wolfberries model, and the accuracy was 96.43% (Cui et al., 2022).…”