1,720,961 research outputs found
Authentication of “Avola almonds” by near infrared (NIR) spectroscopy and chemometrics
No full textAvola almond is part of the “Traditional Italian Agri-food Product” (PAT) list, as established by The Italian Ministry of agricultural food, forestry and tourism policies; this endorsement testifies its status as a high added-value product, and, consequently, it highlights the need of analytical methodologies suitable for its authentication. For these reasons, in the present study, the possibility of developing a non-destructive approach, aimed at distinguishing almonds cultivated in the Avola area from others presenting a different geographical origin, has been investigated. To fulfil this purpose, 227 almonds, cultivated in the Avola area or in other Italian territories, have been analysed by near infrared (NIR) spectroscopy coupled with Partial Least Squares-Discriminant Analysis (PLS-DA) and Soft Independent Modelling of Class Analogies (SIMCA). The two tested approaches achieved satisfactory results (in external validation) indicating both of them would represent a suitable tool for the purpose of the study. © 2019 Elsevier Inc
Identification and quantification of turmeric adulteration in egg-pasta by near infrared spectroscopy and chemometrics
Full text link"Egg pasta" is a kind of pasta prepared by adding eggs in the dough; the color of this product is often associated to its quality, as it is proportional to the quantity of egg present in the dough. A possible adulteration on this product is represented by the addition of turmeric (not reported in the label) in the dough. The inclusion of this ingredient (which is minimal, given the strong coloring power of this spice) fraudulently accentuates the yellow color of the product, making it more attractive to the consumer. Given this scenario, the aim of the present work is to develop an analytical approach suitable at detecting the presence of turmeric as an adulterant in egg pasta. One hundred samples of traditional and adulterated egg pasta were analyzed by NIR spectroscopy and PLS-DA (Partial Least Squares Discriminant Analysis) in order to discriminate adulterated and compliant pasta. The classification model provided a total correct classification rate of 97.5% in external validation (40 samples). Eventually, the adulterant was quantified by PLS. This strategy provided satisfying results, achieving a RMSEP (Root Mean Square Error in Prediction) of 0.112 (%-w/w) in external validation
Authentication of Sorrento walnuts by NIR spectroscopy coupled with different chemometric classification strategie
Full text linkWalnuts have been widely investigated because of their chemical composition, which is particularly rich in unsaturated fatty acids, responsible for different benefits in the human body. Some of these fruits, depending on the harvesting area, are considered a high value-added food, thus resulting in a higher selling price. In Italy, walnuts are harvested throughout the national territory, but the fruits produced in the Sorrento area (South Italy) are commercially valuable for their peculiar organoleptic characteristics. The aim of the present study is to develop a non-destructive and shelf-life compatible method, capable of discriminating common walnuts from those harvested in Sorrento (a town in Southern Italy), considered a high quality product. Two-hundred-and-twenty-seven walnuts (105 from Sorrento and 132 grown in other areas) were analyzed by near-infrared spectroscopy (both whole or shelled), and classified by Partial Least Squares-Discriminant Analysis (PLS-DA). Eventually, two multi-block approaches have been exploited in order to combine the spectral information collected on the shell and on the kernel. One of these latter strategies provided the best results (98.3% of correct classification rate in external validation, corresponding to 1 misclassified object over 60). The present study suggests the proposed strategy is a suitable solution for the discrimination of Sorrento walnuts. © 2020 by the authors
Determination of insect infestation on stored rice by near infrared (NIR) spectroscopy
No full textAmong grains, rice is one of the most widely consumed cereals in the world; it represents a staple food in great part of Asia and Africa, and it is also broadly diffused in America and Europe. One of the main issues of storing rice is to protect it from animal attacks; in particular, it is prone to insect infestation. Despite all the attempts made to avoid it (developing new physical barriers, traps and repellants), often food pests manage to break into granary and parcels, contaminating stored commodities. As a consequence, possible infestations must be continuously checked by producers and/or retailers. Different methods have been developed to detect insects in stored commodities, and, despite some of them demonstrated to perform well, they present the substantial limitation of being destructive. This latter characteristic undoubtedly leads to an obvious loss of product (and consequently, of profit), affecting farmers, retailers, and, finally, consumers. For this reason, the aim of the present work is to develop a methodology for the identification of insect infestation in stored rice by NIR spectroscopy coupled with discriminant and modeling classification methods. In particular, among all the different pests possibly present in granaries, the focus has been on detection of the Indian-meal moth (Plodia interpunctella), because it is considered one of the most common infesting insects. Different samples of rice, both infested and edible, coming from different farmers located in six different Countries (Cambodia, India, Italy, Pakistan, Suriname and Thailand) have been analyzed by NIR spectroscopy. Consequently, two different classification methods, Partial Least Squares Discriminant Analysis (PLS-DA) and Soft Independent Modeling of Class Analogy (SIMCA) have been applied in order to distinguish among infested and edible samples. In particular, PLS-DA allows correctly classifying 95.6% of the edible 97.5% of the contaminated samples (on the external validation set), whereas the SIMCA model, built only for the category of non-contaminated individuals, resulted highly specific (about 97%) but poorly sensitive on the test specimens. This latter approach (SIMCA) provided better predictions (in particular, in terms of sensitivity) when separate individual models were built subdividing samples in agreement with their country of origin. © 2018 Elsevier B.V
MCR-ALS of hyperspectral images with spatio-spectral fuzzy clustering constraint
No full textIn recent years, in the context of the application of Multivariate Curve Resolution (MCR) to hyperspectral image analysis, attention has been more and more put onto the possibility of exploiting not only the spectral but also the spatial information for constraining the algorithmic solution. Examples involve the introduction of different spatial constraints during the iterative Alternating Least Squares (ALS) calculation of the MCR solution or the post-processing of the score images using conventional image processing techniques. In this framework, this work proposes an approach for constraining concentration distribution maps within MCR-ALS analysis of hyperspectral images, based on the use of spatio-spectral fuzzy clustering in order to obtain smoother, more contrasted, and better interpretable chemical images. We show the relevance of the proposed approach and investigate the effect of the application of a spectral-spatial fuzzy clustering constraint on samples of different nature
Multi-block classification of Italian semolina based on Near Infrared Spectroscopy (NIR) analysis and alveographic indices
No full textDurum wheat (Triticum turgidum ssp. durum) is widely grown in the Mediterranean area. The semolina obtained by this grain is used to prepare pasta, couscous, and baked products all over the world. The growing area affects the characteristics of Durum wheat; consequently, it is relevant to trace this product. The present study aims at developing an analytical methodology which would allow tracing durum semolina harvested in 7 different Italian macro-areas. In order to achieve this goal, 597 samples of semolina have been analysed by Near Infrared Spectroscopy, and by measuring alveographic parameters. Eventually, the information collected have been handled by a multi-block classifier (SO-PLS-LDA) in order to predict the origin of samples. The proposed approach provided extremely satisfactory results (in external validation, on a test set of 140 objects), correctly classifying all samples according to their growing area, confirming it represents a suitable solution for tracing durum wheat semolina
Near infrared (NIR) spectroscopy-based classification for the authentication of Darjeeling black tea
No full textDarjeeling black tea is a worldwide known tea variety which is currently part of the register of protected designations of origin (PDO) and protected geographical indications (PGI) as established by Commission Implementing Regulation (EU) No 1050/2011 of 20 October 2011. Therefore, preventing frauds against this product became increasingly important in order to protect producers and consumers from possible economic losses. Starting from this assumption, the present work aims at two different goals: the first one is to develop a rapid, non-destructive and relatively cheap method to distinguish PGI Darjeeling tea from other kinds of black teas, and the second one is to test a non-invasive approach suitable to detect adulterated Darjeeing tea samples. To achieve this goals, NIR spectroscopy has been coupled with two different classifiers: Partial Least Squares-Discriminant Analysis (PLS-DA) and Soft Independent Modelling of Class Analogies (SIMCA). Both provided satisfactory results in discriminating PGI samples from the other teas and from the adulterated Darjeeling
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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