Raman spectroscopy coupled with chemometric modeling approaches for authentication of different paprika varieties at physiological maturity

Analysis of spatiotemporal changes mechanism of cell wall biopolymers and monosaccharide components in kiwifruit during Botryosphaeria dothidea infection

To further reveal the interaction mechanism between plants and pathogens, this study used confocal Raman microscopy spectroscopy (CRM) combined with chemometrics to visualize the biopolymers distribution of kiwifruit cell walls at different infection stages at the cellular micro level. Simultaneously, the changes in the content of various monosaccharides in fruit were studied at the molecular level using high-performance liquid chromatography (HPLC). There were significant differences in the composition of various nutrient components in the cell wall structure of kiwifruit at different infection times after infection by Botryosphaeria dothidea. PCA could cluster samples with infection time of 0-9 d into different infection stages, and SVM was used to predict the PCA classification results, the accuracy >96 %. Multivariate curve resolution-alternating least squares (MCR-ALS) helped to identify single substance spectra and concentration signals from mixed spectral signals. The pure substance chemical imaging maps of low methylated pectin (LMP), high methylated pectin (HMP), cellulose, hemicellulose, and lignin were obtained by analyzing the resolved concentration data. The imaging results showed that the lignin content in the kiwifruit cell wall increased significantly to resist pathogens infection after the infection of B. dothidea. With the development of infection, B. dothidea decomposed various substances in the host cell walls, allowing them to penetrate the interior of fruit cells. This caused significant changes in the form, structure, and distribution of various chemicals on the fruit cell walls in time and space. HPLC showed that glucose was the main carbon source and energy substance obtained by pathogens from kiwifruit during infection. The contents of galactose and arabinose, which maintained the structure and function of the fruit cell walls, decreased significantly and the cell wall structure was destroyed in the late stage of pathogens infection. This study provided a new perspective on the cellular structure changes caused by pathogenic infection of fruit and the defense response process of fruit and provided effective references for further research on the mechanisms of host-pathogen interactions in fruit infected by pathogens.

Alginate Gel-Based Carriers for Encapsulation of Carotenoids: On Challenges and Applications

Sodium alginate is one of the most interesting and the most investigated and applied biopolymers due to its advantageous properties. Among them, easy, simple, mild, rapid, non-toxic gelation by divalent cations is the most important. In addition, it is abundant, low-cost, eco-friendly, bio-compatible, bio-adhesive, biodegradable, stable, etc. All those properties were systematically considered within this review. Carotenoids are functional components in the human diet with plenty of health benefits. However, their sensitivity to environmental and process stresses, chemical instability, easy oxidation, low water solubility, and bioavailability limit their food and pharmaceutical applications. Encapsulation may help in overcoming these limitations and within this review, the role of alginate-based encapsulation systems in improving the stability and bioavailability of carotenoids is explored. It may be concluded that all alginate-based systems increase carotenoid stability, but only those of micro- and nano-size, as well as emulsion-based, may improve their low bioaccessibility. In addition, the incorporation of other biopolymers may further improve encapsulation system properties. Furthermore, the main techniques for evaluating the encapsulation are briefly considered. This review critically and profoundly explains the role of alginates in improving the encapsulation process of carotenoids, suggesting the best alternatives for those systems. Moreover, it provides a comprehensive cover of recent advances in this field.

Detection and Quantitation of Adulterated Paprika Samples Using Second-Order HPLC-FLD Fingerprints and Chemometrics

Paprika is a widely consumed spice in the world and its authentication has gained interest considering the increase in adulteration cases in recent years. In this study, second-order fingerprints acquired by liquid chromatography with fluorescence detection (HPLC-FLD) were first used to detect and quantify adulteration levels of Chinese paprika samples. Six different adulteration cases, involving paprika production region, cultivar, or both, were investigated by pairs. Two strategies were employed to reduce the data matrices: (1) chromatographic fingerprints collected at specific wavelengths and (2) fusion of the mean data profiles in both spectral and time dimensions. Afterward, the fingerprint data with different data orders were analyzed using partial least squares (PLS) and n-way partial least squares (N-PLS) regression models, respectively. For most adulteration cases, N-PLS based on second-order fingerprints provided the overall best quantitation results with cross-validation and prediction errors lower than 2.27% and 20.28%, respectively, for external validation sets with 15-85% adulteration levels. To conclude, second-order HPLC-FLD fingerprints coupled with chemometrics can be a promising screening technique to assess paprika quality and authenticity in the control and prevention of food frauds.

Discriminant Analysis PCA-LDA Assisted Surface-Enhanced Raman Spectroscopy for Direct Identification of Malaria-Infected Red Blood Cells

Various methods for detecting malaria have been developed in recent years, each with its own set of advantages. These methods include microscopic, antigen-based, and molecular-based analysis of blood samples. This study aimed to develop a new, alternative procedure for clinical use by using a large data set of surface-enhanced Raman spectra to distinguish normal and infected red blood cells. PCA-LDA algorithms were used to produce models for separating P. falciparum (3D7)-infected red blood cells and normal red blood cells based on their Raman spectra. Both average normalized spectra and spectral imaging were considered. However, these initial spectra could hardly differentiate normal cells from the infected cells. Then, discrimination analysis was applied to assist in the classification and visualization of the different spectral data sets. The results showed a clear separation in the PCA-LDA coordinate. A blind test was also carried out to evaluate the efficiency of the PCA-LDA separation model and achieved a prediction accuracy of up to 80%. Considering that the PCA-LDA separation accuracy will improve when a larger set of training data is incorporated into the existing database, the proposed method could be highly effective for the identification of malaria-infected red blood cells.