Discrimination of five Tunisian cultivars by Mid InfraRed spectroscopy combined with chemometric analyses of olive Olea europaea leaves

Near-Infrared Spectroscopy Machine-Learning Spectral Analysis Tool for Blueberries (Vaccinium corymbosum) Cultivar Discrimination

Vaccinium corymbosum is one of the main sources of commercialized blueberries across the world. This species has a large number of distinct cultivars, leading to significantly different berries characteristics such as size, sweetness, production rate, and growing season. In this context, accurate cultivar discrimination is of significant relevance, and currently, it is mostly performed through berries examination. In this work, we developed a method to discriminate 19 cultivars from the V. corymbosum species through their leaves' near-infrared spectra. Spectra were acquired from fresh blueberry leaves collected from two geographic regions and across three seasons. Machine-learning-based models, selected from a pool of 10 classifiers based on their discrimination power under a twofold stratified cross-validation process, were trained/tested with 1 to 20 components obtained by the application of data dimensionality reduction (DDR) techniques (dictionary learning, factor analysis, fast individual component analysis, and principal component analysis) to different near-infrared (NIR) spectra regions' data, to either analyze a single spectral region and season or combine spectral regions and/or seasons for each side of the blueberry leaf. The percentage of correct cultivar discrimination ranged from 52.27 to 100%, with the best spectral results obtained with the adaxial side of the leaves in the fall (100% Accuracy) and the abaxial side of the leaves in the winter (100% Accuracy); the fast ICA DDR technique was present in 83.33% of the best analyses (five out of six); and the LinearSVC was present in 66.67% (four out six best analyses). The results obtained in this work denote that near-infrared spectroscopy is a suitable and accurate technique for V. corymbosum cultivar discrimination.

As a novel anticancer candidate, ether extract of Dendrobium nobile overstimulates cellular protein biosynthesis to induce cell stress and autophagy

Increasing data has confirmed the potential anticancer properties of Dendrobium, a traditional Chinese herb. However, most anticancer compositions from the plant of Dendrobium were usually extracted by high polar solvent, while weak polar compositions with excellent anticancer activity remained largely unexplored. In this study, the differences between ether extract and ethanol extract of Dendrobium nobile Lindl. on chemical components and anticancer activities were investigated, as well as the anticancer mechanisms among different extracts. The results demonstrated that the ether extract exhibited a stronger anticancer effect than ethanol extract, and its anticancer effect was mainly due to weak polar compounds rather than polysaccharides and alkaloids. Quantitative proteomics suggested that the ether extract significantly stimulated the over-expression of immature proteins, the endoplasmic reticulum stress and unfolded protein response were subsequently induced, the intracellular reactive oxygen species level was seriously elevated, and oxidative stress occurred in the meanwhile. Eventually, autophagy and apoptosis were activated to cause cell death. Our findings demonstrate that the ether extract of D. nobile is a potential candidate for anticancer drug development, and that future research on anticancer drugs derived from medicinal plants should also concentrate on weak polar compounds.

Classification of Soybean Genotypes Assessed Under Different Water Availability and at Different Phenological Stages Using Leaf-Based Hyperspectral Reflectance

Monitoring of soybean genotypes is important because of intellectual property over seed technology, better management over seed genetics, and more efficient strategies for its agricultural production process. This paper aims at spectrally classifying soybean genotypes submitted to diverse water availability levels at different phenological stages using leaf-based hyperspectral reflectance. Leaf reflectance spectra were collected using a hyperspectral proximal sensor. Two experiments were conducted as field trials: one experiment was at Embrapa Soja in the 2016/2017, 2017/2018, and 2018/2019 cropping seasons, where ten soybean genotypes were grown under four water conditions; and another experiment was in the experimental farm of Unoeste University in the 2018/2019 cropping season, where nine soybean genotypes were evaluated. The spectral data collected was divided into nine spectral datasets, comprising single and multiple cropping seasons (from 2016 to 2019), and two contrasting crop-growing environments. Principal component analysis, applied as an indicator of the explained variance of the reflectance spectra among genotypes within each spectral dataset, explained over 94% of the spectral variance in the first three principal components. Linear discriminant analysis, used to obtain a model of classification of each reflectance spectra of soybean leaves into each soybean genotype, achieved accuracy between 61% and 100% in the calibration procedure and between 50% and 100% in the validation procedure. Misclassification was observed only between genotypes from the same genetic background. The results demonstrated the great potential of the spectral classification of soybean genotypes at leaf-scale, regardless of the phenological stages or water status to which plants were submitted.

Implementation of chemometrics in quality evaluation of food and beverages

Conventional methods for food quality evaluation based on chemical or microbiological analysis followed by traditional univariate statistics such as ANOVA are considered insufficient for some purposes. More sophisticated instrumental methods including spectroscopy and chromatography, in combination with multivariate analysis-chemometrics, can be used to determine food authenticity, identify adulterations or mislabeling and determine food safety. The purpose of this review is to present the current state of knowledge on the use of chemometric tools for evaluating quality of food products of animal and plant origin and beverages. The article describes applications of several multivariate techniques in food and beverages research, showing their role in adulteration detection, authentication, quality control, differentiation of samples and comparing their classification and prediction ability.

Olive Tree (Olea europeae L.) Leaves: Importance and Advances in the Analysis of Phenolic Compounds

Phenolic compounds are becoming increasingly popular because of their potential role in contributing to human health. Experimental evidence obtained from human and animal studies demonstrate that phenolic compounds from Olea europaea leaves have biological activities which may be important in the reduction in risk and severity of certain chronic diseases. Therefore, an accurate profiling of phenolics is a crucial issue. In this article, we present a review work on current treatment and analytical methods used to extract, identify, and/or quantify phenolic compounds in olive leaves.

Fourier transform infrared spectroscopy combined with chemometrics for discrimination of Curcuma longa, Curcuma xanthorrhiza and Zingiber cassumunar

Turmeric (Curcuma longa), java turmeric (Curcuma xanthorrhiza) and cassumunar ginger (Zingiber cassumunar) are widely used in traditional Indonesian medicines (jamu). They have similar color for their rhizome and possess some similar uses, so it is possible to substitute one for the other. The identification and discrimination of these closely-related plants is a crucial task to ensure the quality of the raw materials. Therefore, an analytical method which is rapid, simple and accurate for discriminating these species using Fourier transform infrared spectroscopy (FTIR) combined with some chemometrics methods was developed. FTIR spectra were acquired in the mid-IR region (4000-400 cm(-1)). Standard normal variate, first and second order derivative spectra were compared for the spectral data. Principal component analysis (PCA) and canonical variate analysis (CVA) were used for the classification of the three species. Samples could be discriminated by visual analysis of the FTIR spectra by using their marker bands. Discrimination of the three species was also possible through the combination of the pre-processed FTIR spectra with PCA and CVA, in which CVA gave clearer discrimination. Subsequently, the developed method could be used for the identification and discrimination of the three closely-related plant species.